WO2013125335A1 - Solid-object detection device and method for detecting solid object - Google Patents
Solid-object detection device and method for detecting solid object Download PDFInfo
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- WO2013125335A1 WO2013125335A1 PCT/JP2013/052477 JP2013052477W WO2013125335A1 WO 2013125335 A1 WO2013125335 A1 WO 2013125335A1 JP 2013052477 W JP2013052477 W JP 2013052477W WO 2013125335 A1 WO2013125335 A1 WO 2013125335A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/215—Motion-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20224—Image subtraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
- G06T2207/30261—Obstacle
Definitions
- the present invention relates to a three-dimensional object detection device and a three-dimensional object detection method.
- This application claims priority based on Japanese Patent Application No. 2012-036300 filed on Feb. 22, 2012.
- the contents described in the application are incorporated into the present application by reference and made a part of the description of the present application.
- An obstacle detection apparatus that detects an obstacle by performing overhead conversion of an image obtained by capturing an image of the surroundings of a vehicle and using a difference between two overhead conversion images that are temporally different (see Patent Document 1).
- an object such as an implantation or guardrail provided on the shoulder of the traveling lane of the own vehicle Is erroneously recognized as an image of another vehicle traveling in the adjacent lane.
- an image of an object placed on the road shoulder enters an area corresponding to the adjacent lane, and thus the above problem is likely to occur.
- the problem to be solved by the present invention is to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle, and traveling in the adjacent lane.
- Another object of the present invention is to provide a three-dimensional object detection device that can detect other vehicles with high accuracy.
- the present invention sets a first detection area when calculating the movement distance of a three-dimensional object, and a second detection area having a smaller area than the first detection area when detecting a three-dimensional object existing in the detection area.
- the present invention sets a relatively wide first detection area when calculating the moving distance of a three-dimensional object, and sets a relatively narrow second detection area when detecting the presence of a three-dimensional object, To prevent the object on the shoulder of the driving lane of the host vehicle from being included in the detection area, and to reduce the possibility that the feature of the object appearing in a predetermined cycle along the shoulder is misidentified as the feature of the other vehicle in the adjacent lane Accordingly, it is possible to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle. As a result, it is possible to provide a three-dimensional object detection device that detects, with high accuracy, other vehicles that travel in the adjacent lane adjacent to the travel lane of the host vehicle.
- FIG. 1 is a schematic configuration diagram of a vehicle according to an embodiment to which a three-dimensional object detection device of the present invention is applied. It is a top view (three-dimensional object detection by difference waveform information) which shows the driving state of the vehicle of FIG. It is a block diagram which shows the detail of the computer of FIG. 4A and 4B are diagrams for explaining the outline of processing of the alignment unit in FIG. 3, in which FIG. 3A is a plan view showing a moving state of the vehicle, and FIG. It is the schematic which shows the mode of the production
- FIG. 4 is a flowchart (No. 1) illustrating a three-dimensional object detection method using differential waveform information executed by the viewpoint conversion unit, the alignment unit, the smear detection unit, and the three-dimensional object detection unit of FIG. 3.
- FIG. 1 is a flowchart (No. 1) illustrating a three-dimensional object detection method using differential waveform information executed by the viewpoint conversion unit, the alignment unit, the smear detection unit, and the three-dimensional object detection unit of FIG. 3.
- FIG. 4 is a flowchart (part 2) illustrating a three-dimensional object detection method using differential waveform information executed by the viewpoint conversion unit, the alignment unit, the smear detection unit, and the three-dimensional object detection unit of FIG. 3. It is a figure (three-dimensional object detection by edge information) which shows the running state of vehicles of Drawing 1, (a) is a top view showing the positional relationship of a detection field etc., and (b) shows the positional relationship of a detection field etc. in real space. It is a perspective view shown. 4A and 4B are diagrams for explaining the operation of the luminance difference calculation unit in FIG. 3, in which FIG.
- 3A is a diagram illustrating a positional relationship among attention lines, reference lines, attention points, and reference points in a bird's eye view image; It is a figure which shows the positional relationship of the attention line, reference line, attention point, and reference point.
- 4A and 4B are diagrams for explaining the detailed operation of the luminance difference calculation unit in FIG. 3, in which FIG. 3A is a diagram illustrating a detection region in a bird's-eye view image, and FIG. It is a figure which shows the positional relationship of a reference point.
- FIG. 4 is a flowchart (part 1) illustrating a three-dimensional object detection method using edge information executed by a viewpoint conversion unit, a luminance difference calculation unit, an edge line detection unit, and a three-dimensional object detection unit in FIG. 3;
- FIG. 4 is a flowchart (part 1) illustrating a three-dimensional object detection method using edge information executed by a viewpoint conversion unit, a luminance difference calculation unit, an edge line detection unit, and a three-dimensional object detection unit in FIG. 3;
- FIG. 4 is a flowchart (part 2) illustrating a three-dimensional object detection method using edge information executed by the viewpoint conversion unit, the luminance difference calculation unit, the edge line detection unit, and the three-dimensional object detection unit of FIG. 3. It is a figure which shows the example of an image for demonstrating edge detection operation
- FIG. 26 is a first diagram for explaining the processing of FIG. 25.
- FIG. 26 is a second diagram for explaining the processing of FIG. 25.
- It is a flowchart of the further another example which shows the control procedure of the setting of a detection area. It is a figure for demonstrating the process of FIG.
- FIG. 1 is a schematic configuration diagram of a vehicle according to an embodiment to which a three-dimensional object detection device 1 of the present invention is applied.
- the three-dimensional object detection device 1 of the present example is careful when the driver of the host vehicle V is driving. Is a device that detects, as an obstacle, other vehicles that are likely to be contacted, for example, other vehicles that may be contacted when the host vehicle V changes lanes.
- the three-dimensional object detection device 1 of this example detects another vehicle that travels in an adjacent lane (hereinafter also simply referred to as an adjacent lane) adjacent to the lane in which the host vehicle travels. Further, the three-dimensional object detection device 1 of the present example can calculate the detected movement distance and movement speed of the other vehicle.
- the three-dimensional object detection device 1 is mounted on the own vehicle V, and the three-dimensional object detected around the own vehicle travels in the adjacent lane next to the lane on which the own vehicle V travels.
- An example of detecting a vehicle will be shown.
- the three-dimensional object detection device 1 of the present example includes a camera 10, a vehicle speed sensor 20, and a calculator 30.
- the camera 10 is attached to the host vehicle V so that the optical axis is at an angle ⁇ from the horizontal to the lower side at a height h at the rear of the host vehicle V.
- the camera 10 images a predetermined area in the surrounding environment of the host vehicle V from this position.
- the vehicle speed sensor 20 detects the traveling speed of the host vehicle V, and calculates the vehicle speed from the wheel speed detected by, for example, a wheel speed sensor that detects the rotational speed of the wheel.
- the computer 30 detects a three-dimensional object behind the vehicle, and calculates a moving distance and a moving speed for the three-dimensional object in this example.
- FIG. 2 is a plan view showing a traveling state of the host vehicle V in FIG.
- the camera 10 images the vehicle rear side at a predetermined angle of view a.
- the angle of view a of the camera 10 is set to an angle of view at which the left and right lanes can be imaged in addition to the lane in which the host vehicle V travels.
- the area that can be imaged includes detection target areas A1 and A2 on the adjacent lane that is behind the host vehicle V and that is adjacent to the left and right of the travel lane of the host vehicle V.
- FIG. 3 is a block diagram showing details of the computer 30 of FIG. In FIG. 3, the camera 10 and the vehicle speed sensor 20 are also shown in order to clarify the connection relationship.
- the computer 30 includes a viewpoint conversion unit 31, a positioning unit 32, a three-dimensional object detection unit 33, a detection area setting unit 34, and a smear detection unit 40.
- the calculation unit 30 of the present embodiment has a configuration relating to a three-dimensional object detection block using differential waveform information.
- the calculation unit 30 of the present embodiment can also be configured with respect to a three-dimensional object detection block using edge information.
- a block configuration A configured by the alignment unit 32 and the three-dimensional object detection unit 33 is surrounded by a broken line, a luminance difference calculation unit 35, an edge line detection unit 36, It can be configured by replacing the block configuration B configured by the three-dimensional object detection unit 37.
- both the block configuration A and the block configuration B can be provided, so that the solid object can be detected using the difference waveform information and the solid object can be detected using the edge information.
- the block configuration A and the block configuration B can be provided, either the block configuration A or the block configuration B can be operated according to environmental factors such as brightness. Each configuration will be described below.
- the three-dimensional object detection device 1 of the present embodiment exists in the detection area A1 of the right adjacent lane or the detection area A2 of the left adjacent lane behind the vehicle based on image information obtained by the monocular camera 1 that captures the rear of the vehicle. A three-dimensional object is detected.
- the detection area setting unit 34 sets detection areas A1 and A2 in the captured image information and on the right and left sides behind the host vehicle V, respectively.
- the positions of the detection areas A2 and A2 are not particularly limited, and can be set as appropriate according to the processing conditions.
- the detection region setting unit 34 of the present embodiment includes a first detection region having a relatively large area and a second detection region having a relatively small area for each of the detection regions A1 and A2, and the first detection region Either the region or the second detection region can be selected and set.
- the detection area setting unit 34 of the present embodiment selects and sets a second detection area having a relatively small area, and detects the speed of the three-dimensional object. In this case, the first detection region having a relatively large area is selected and set.
- the three-dimensional object detection unit 33 determines the presence / absence of the three-dimensional object based on the image information of the second detection region having a relatively small area set by the detection region setting unit 34.
- the moving speed is calculated based on the image information of the first detection area having a relatively large area set by the detection area setting unit 34.
- the detection area setting unit 34 can set the first detection area and the second detection area in response to a request from the three-dimensional object detection unit 33 (37).
- the detection area setting unit 34 of the present embodiment sets the second detection area of the detection area A1 set on the right side behind the vehicle on the right side behind the vehicle.
- the first detection area the area including the apex located at the right end on the front side of the vehicle is deleted, and the second detection area of the detection area A2 set on the left side on the rear side of the vehicle is set on the left side on the rear side of the vehicle. It is assumed that the region including the apex located at the left end on the front side of the vehicle in the set first detection region is a missing region.
- the detection region setting unit 34 of the present embodiment selects and sets a second detection region in which a region including a vertex located at the right end or the left end is missing.
- the first detection region in which the region including the vertex is not lost is selected and set.
- the detection area setting unit 34 of the present embodiment sets the first detection area and the position of the camera 10 in the lateral direction of the vehicle from the first detection area.
- a detection area including a second detection area in which the distance is greater than or equal to a predetermined distance, that is, an area outside the first detection area (the road shoulder side) is missing (narrowed in the vehicle width direction) is set.
- the viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging with the camera 10 and converts the input captured image data into a bird's-eye image data in a bird's-eye view state.
- the state viewed from a bird's-eye view is a state viewed from the viewpoint of a virtual camera looking down from above, for example, vertically downward.
- This viewpoint conversion can be executed as described in, for example, Japanese Patent Application Laid-Open No. 2008-219063.
- the viewpoint conversion of captured image data to bird's-eye view image data is based on the principle that a vertical edge peculiar to a three-dimensional object is converted into a straight line group passing through a specific fixed point by viewpoint conversion to bird's-eye view image data. This is because a planar object and a three-dimensional object can be distinguished if used. Note that the result of the image conversion processing by the viewpoint conversion unit 31 is also used in detection of a three-dimensional object by edge information described later.
- the alignment unit 32 sequentially inputs the bird's-eye image data obtained by the viewpoint conversion of the viewpoint conversion unit 31, and aligns the positions of the inputted bird's-eye image data at different times.
- 4A and 4B are diagrams for explaining the outline of the processing of the alignment unit 32, where FIG. 4A is a plan view showing the moving state of the host vehicle V, and FIG. 4B is an image showing the outline of the alignment.
- the host vehicle V at the current time is located at V1, and the host vehicle V one hour before is located at V2.
- the other vehicle VX is located in the rear direction of the own vehicle V and is in parallel with the own vehicle V, the other vehicle VX at the current time is located at V3, and the other vehicle VX one hour before is located at V4.
- the host vehicle V has moved a distance d at one time.
- “one hour before” may be a past time for a predetermined time (for example, one control cycle) from the current time, or may be a past time for an arbitrary time.
- the bird's-eye image PB t at the current time is as shown in Figure 4 (b).
- the bird's-eye image PB t becomes a rectangular shape for the white line drawn on the road surface, but a relatively accurate is a plan view state, tilting occurs about the position of another vehicle VX at position V3.
- the white line drawn on the road surface has a rectangular shape and is relatively accurately viewed in plan, but the other vehicle VX at the position V4 Falls down.
- the vertical edges of solid objects are straight lines along the collapse direction by the viewpoint conversion processing to bird's-eye view image data. This is because the plane image on the road surface does not include a vertical edge, but such a fall does not occur even when the viewpoint is changed.
- the alignment unit 32 performs alignment of the bird's-eye images PB t and PB t ⁇ 1 as described above on the data. At this time, the alignment unit 32 is offset a bird's-eye view image PB t-1 before one unit time, to match the position and bird's-eye view image PB t at the current time.
- the image on the left side and the center image in FIG. 4B show a state that is offset by the movement distance d ′.
- This offset amount d ′ is a movement amount on the bird's-eye view image data corresponding to the actual movement distance d of the host vehicle V shown in FIG. It is determined based on the time until the time.
- the alignment unit 32 takes the difference between the bird's-eye images PB t and PB t ⁇ 1 and generates data of the difference image PD t .
- the pixel value of the difference image PD t may be an absolute value of the difference between the pixel values of the bird's-eye images PB t and PB t ⁇ 1 , and the absolute value is predetermined in order to cope with a change in the illuminance environment. It may be set to “1” when the threshold value p is exceeded and “0” when the threshold value p is not exceeded.
- the image on the right side of FIG. 4B is the difference image PD t .
- the three-dimensional object detection unit 33 detects a three-dimensional object based on the data of the difference image PD t shown in FIG. At this time, the three-dimensional object detection unit 33 of this example also calculates the movement distance of the three-dimensional object in the real space. In detecting the three-dimensional object and calculating the movement distance, the three-dimensional object detection unit 33 first generates a differential waveform. Note that the moving distance of the three-dimensional object per time is used for calculating the moving speed of the three-dimensional object. The moving speed of the three-dimensional object can be used to determine whether or not the three-dimensional object is a vehicle.
- Three-dimensional object detection unit 33 of the present embodiment when generating the differential waveform sets a detection area in the difference image PD t.
- the three-dimensional object detection device 1 of the present example is another vehicle that the driver of the host vehicle V pays attention to, in particular, the lane in which the host vehicle V that may be contacted when the host vehicle V changes lanes travels. Another vehicle traveling in the adjacent lane is detected as a detection target. For this reason, in this example which detects a solid object based on image information, two detection areas are set on the right side and the left side of the host vehicle V in the image obtained by the camera 1. Specifically, in the present embodiment, rectangular detection areas A1 and A2 are set on the left and right sides behind the host vehicle V as shown in FIG.
- the other vehicle detected in the detection areas A1 and A2 is detected as an obstacle traveling in the adjacent lane adjacent to the lane in which the host vehicle V is traveling.
- Such detection areas A1 and A2 may be set from a relative position with respect to the host vehicle V, or may be set based on the position of the white line.
- the movement distance detection device 1 may use, for example, an existing white line recognition technique.
- the three-dimensional object detection unit 33 recognizes the sides (sides along the traveling direction) of the set detection areas A1 and A2 on the own vehicle V side as the ground lines L1 and L2 (FIG. 2).
- the ground line means a line in which the three-dimensional object contacts the ground.
- the ground line is set as described above, not a line in contact with the ground. Even in this case, from experience, the difference between the ground line according to the present embodiment and the ground line obtained from the position of the other vehicle VX is not too large, and there is no problem in practical use.
- FIG. 5 is a schematic diagram illustrating how a differential waveform is generated by the three-dimensional object detection unit 33 illustrated in FIG. 3.
- the three-dimensional object detection unit 33 calculates a differential waveform from a portion corresponding to the detection areas A ⁇ b> 1 and A ⁇ b> 2 in the difference image PD t (right diagram in FIG. 4B) calculated by the alignment unit 32.
- DW t is generated.
- the three-dimensional object detection unit 33 generates a differential waveform DW t along the direction in which the three-dimensional object falls by viewpoint conversion.
- the difference waveform DW t is generated for the detection area A2 in the same procedure.
- the three-dimensional object detection unit 33 defines a line La in the direction in which the three-dimensional object falls on the data of the difference image DW t . Then, the three-dimensional object detection unit 33 counts the number of difference pixels DP indicating a predetermined difference on the line La.
- the difference pixel DP indicating the predetermined difference has a predetermined threshold value when the pixel value of the difference image DW t is an absolute value of the difference between the pixel values of the bird's-eye images PB t and PB t ⁇ 1. If the pixel value of the difference image DW t is expressed as “0” or “1”, the pixel indicates “1”.
- the three-dimensional object detection unit 33 counts the number of difference pixels DP and then obtains an intersection point CP between the line La and the ground line L1. Then, the three-dimensional object detection unit 33 associates the intersection CP with the count number, determines the horizontal axis position based on the position of the intersection CP, that is, the position on the vertical axis in the right diagram of FIG. The axis position, that is, the position on the right and left axis in the right diagram of FIG. 5 is determined and plotted as the count number at the intersection CP.
- the three-dimensional object detection unit 33 defines lines Lb, Lc... In the direction in which the three-dimensional object falls, counts the number of difference pixels DP, and determines the horizontal axis position based on the position of each intersection CP. Then, the vertical axis position is determined from the count number (number of difference pixels DP) and plotted.
- the three-dimensional object detection unit 33 generates the differential waveform DW t as shown in the right diagram of FIG.
- the line La and the line Lb in the direction in which the three-dimensional object collapses have different distances overlapping the detection area A1. For this reason, if the detection area A1 is filled with the difference pixels DP, the number of difference pixels DP is larger on the line La than on the line Lb. For this reason, when the three-dimensional object detection unit 33 determines the vertical axis position from the count number of the difference pixels DP, the three-dimensional object detection unit 33 is normalized based on the distance at which the lines La and Lb in the direction in which the three-dimensional object falls and the detection area A1 overlap. Turn into. As a specific example, in the left diagram of FIG.
- the three-dimensional object detection unit 33 normalizes the count number by dividing it by the overlap distance.
- the difference waveform DW t the line La on the direction the three-dimensional object collapses, the value of the differential waveform DW t corresponding to Lb is substantially the same.
- the three-dimensional object detection unit 33 calculates the movement distance by comparison with the differential waveform DW t ⁇ 1 one time before. That is, the three-dimensional object detection unit 33 calculates the movement distance from the time change of the difference waveforms DW t and DW t ⁇ 1 .
- the three-dimensional object detection unit 33 divides the differential waveform DW t into a plurality of small areas DW t1 to DW tn (n is an arbitrary integer equal to or greater than 2).
- FIG. 6 is a diagram illustrating the small areas DW t1 to DW tn divided by the three-dimensional object detection unit 33.
- the small areas DW t1 to DW tn are divided so as to overlap each other, for example, as shown in FIG. For example, the small area DW t1 and the small area DW t2 overlap, and the small area DW t2 and the small area DW t3 overlap.
- the three-dimensional object detection unit 33 obtains an offset amount (amount of movement of the differential waveform in the horizontal axis direction (vertical direction in FIG. 6)) for each of the small areas DW t1 to DW tn .
- the offset amount is determined from the difference between the differential waveform DW t in the difference waveform DW t-1 and the current time before one unit time (distance in the horizontal axis direction).
- three-dimensional object detection unit 33 for each small area DW t1 ⁇ DW tn, when moving the differential waveform DW t1 before one unit time in the horizontal axis direction, the differential waveform DW t at the current time The position where the error is minimized (the position in the horizontal axis direction) is determined, and the amount of movement in the horizontal axis between the original position of the differential waveform DW t ⁇ 1 and the position where the error is minimized is obtained as an offset amount. Then, the three-dimensional object detection unit 33 counts the offset amount obtained for each of the small areas DW t1 to DW tn and forms a histogram.
- FIG. 7 is a diagram illustrating an example of a histogram obtained by the three-dimensional object detection unit 33.
- the offset amount which is the amount of movement that minimizes the error between each of the small areas DW t1 to DW tn and the differential waveform DW t ⁇ 1 one time before, has some variation.
- the three-dimensional object detection unit 33 forms a histogram of offset amounts including variations, and calculates a movement distance from the histogram.
- the three-dimensional object detection unit 33 calculates the moving distance of the three-dimensional object from the maximum value of the histogram. That is, in the example illustrated in FIG.
- the three-dimensional object detection unit 33 calculates the offset amount indicating the maximum value of the histogram as the movement distance ⁇ * .
- the moving distance ⁇ * is a relative moving distance of the other vehicle VX with respect to the host vehicle V. For this reason, when calculating the absolute movement distance, the three-dimensional object detection unit 33 calculates the absolute movement distance based on the obtained movement distance ⁇ * and the signal from the vehicle speed sensor 20.
- the three-dimensional object detection unit 33 weights each of the plurality of small areas DW t1 to DW tn and forms a histogram by counting the offset amount obtained for each of the small areas DW t1 to DW tn according to the weight. May be.
- FIG. 8 is a diagram illustrating weighting by the three-dimensional object detection unit 33.
- the small area DW m (m is an integer of 1 to n ⁇ 1) is flat. That is, in the small area DW m , the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference is small. Three-dimensional object detection unit 33 to reduce the weight for such small area DW m. This is because the flat small area DW m has no characteristics and is likely to have a large error in calculating the offset amount.
- the small region DW m + k (k is an integer equal to or less than nm) is rich in undulations. That is, in the small area DW m , the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference is large.
- Three-dimensional object detection unit 33 increases the weight for such small area DW m. This is because the small region DW m + k rich in undulations is characteristic and there is a high possibility that the offset amount can be accurately calculated. By weighting in this way, the calculation accuracy of the movement distance can be improved.
- the differential waveform DW t is divided into a plurality of small areas DW t1 to DW tn in order to improve the calculation accuracy of the movement distance.
- the small area DW t1 is divided. It is not necessary to divide into ⁇ DW tn .
- the three-dimensional object detection unit 33 calculates the moving distance from the offset amount of the differential waveform DW t when the error between the differential waveform DW t and the differential waveform DW t ⁇ 1 is minimized. That is, the method for obtaining the offset amount of the difference waveform DW t in the difference waveform DW t-1 and the current time before one unit time is not limited to the above disclosure.
- the computer 30 includes a smear detection unit 40.
- the smear detection unit 40 detects a smear generation region from data of a captured image obtained by imaging with the camera 10. Since smear is a whiteout phenomenon that occurs in a CCD image sensor or the like, the smear detection unit 40 may be omitted when the camera 10 using a CMOS image sensor or the like that does not generate such smear is employed.
- FIG. 9 is an image diagram for explaining the processing by the smear detection unit 40 and the calculation processing of the differential waveform DW t thereby.
- data of the captured image P in which the smear S exists is input to the smear detection unit 40.
- the smear detection unit 40 detects the smear S from the captured image P.
- There are various methods for detecting the smear S For example, in the case of a general CCD (Charge-Coupled Device) camera, the smear S is generated only in the downward direction of the image from the light source.
- CCD Charge-Coupled Device
- a region having a luminance value equal to or higher than a predetermined value from the lower side of the image to the upper side of the image and continuous in the vertical direction is searched, and this is identified as a smear S generation region.
- the smear detection unit 40 generates smear image SP data in which the pixel value is set to “1” for the place where the smear S occurs and the other place is set to “0”. After the generation, the smear detection unit 40 transmits the data of the smear image SP to the viewpoint conversion unit 31.
- the viewpoint conversion unit 31 to which the data of the smear image SP is input converts the viewpoint into a state of bird's-eye view.
- the viewpoint conversion unit 31 generates data of the smear bird's-eye view image SB t .
- the viewpoint conversion unit 31 transmits the data of the smear bird's-eye view image SB t to the alignment unit 33.
- the viewpoint conversion unit 31 transmits the data of the smear bird's-eye view image SB t ⁇ 1 one hour before to the alignment unit 33.
- the alignment unit 32 aligns the smear bird's-eye images SB t and SB t ⁇ 1 on the data.
- the specific alignment is the same as the case where the alignment of the bird's-eye images PB t and PB t ⁇ 1 is executed on the data.
- the alignment unit 32 performs a logical sum on the smear S generation region of each smear bird's-eye view image SB t , SB t ⁇ 1 . Thereby, the alignment part 32 produces
- the alignment unit 32 transmits the data of the mask image MP to the three-dimensional object detection unit 33.
- the three-dimensional object detection unit 33 sets the count number of the frequency distribution to zero for the portion corresponding to the smear S generation region in the mask image MP. That is, when the differential waveform DW t as shown in FIG. 9 is generated, the three-dimensional object detection unit 33 sets the count number SC by the smear S to zero and generates a corrected differential waveform DW t ′. Become.
- the three-dimensional object detection unit 33 obtains the moving speed of the vehicle V (camera 10), and obtains the offset amount for the stationary object from the obtained moving speed. After obtaining the offset amount of the stationary object, the three-dimensional object detection unit 33 calculates the moving distance of the three-dimensional object after ignoring the offset amount corresponding to the stationary object among the maximum values of the histogram.
- FIG. 10 is a diagram illustrating another example of a histogram obtained by the three-dimensional object detection unit 33.
- a stationary object exists in addition to the other vehicle VX within the angle of view of the camera 10, two maximum values ⁇ 1 and ⁇ 2 appear in the obtained histogram.
- one of the two maximum values ⁇ 1, ⁇ 2 is the offset amount of the stationary object.
- the three-dimensional object detection unit 33 calculates the offset amount for the stationary object from the moving speed, ignores the maximum value corresponding to the offset amount, and calculates the moving distance of the three-dimensional object by using the remaining maximum value. To do.
- the three-dimensional object detection unit 33 stops calculating the movement distance.
- step S0 the computer 30 sets a detection area based on a predetermined rule. This detection area setting method will be described in detail later.
- the computer 30 receives data of the image P captured by the camera 10 and generates a smear image SP by the smear detection unit 40 (S1).
- the viewpoint conversion unit 31 generates data of the bird's-eye view image PB t from the data of the captured image P from the camera 10, and also generates data of the smear bird's-eye view image SB t from the data of the smear image SP (S2).
- the alignment unit 33 aligns the data of the bird's-eye view image PB t and the data of the bird's-eye view image PB t-1 of the previous time, and the data of the smear bird's-eye view image SB t and the smear bird's-eye view of the previous time.
- the data of the image SB t-1 is aligned (S3).
- the alignment unit 33 generates data for the difference image PD t and also generates data for the mask image MP (S4).
- three-dimensional object detection unit 33, the data of the difference image PD t, and a one unit time before the difference image PD t-1 of the data generates a difference waveform DW t (S5).
- the three-dimensional object detection unit 33 After generating the differential waveform DW t , the three-dimensional object detection unit 33 sets the count number corresponding to the smear S generation region in the differential waveform DW t to zero, and suppresses the influence of the smear S (S6).
- the three-dimensional object detection unit 33 determines whether or not the peak of the differential waveform DW t is greater than or equal to the first threshold value ⁇ (S7).
- the peak of the difference waveform DW t is not equal to or greater than the first threshold value ⁇ , that is, when there is almost no difference, it is considered that there is no three-dimensional object in the captured image P.
- the three-dimensional object detection unit 33 does not have a three-dimensional object and has another vehicle as an obstacle. It is determined not to do so (FIG. 12: S16). Then, the processes shown in FIGS. 11 and 12 are terminated.
- the three-dimensional object detection unit 33 determines that a three-dimensional object exists, and sets the difference waveform DW t to a plurality of difference waveforms DW t .
- the area is divided into small areas DW t1 to DW tn (S8).
- the three-dimensional object detection unit 33 performs weighting for each of the small areas DW t1 to DW tn (S9).
- the three-dimensional object detection unit 33 calculates an offset amount for each of the small areas DW t1 to DW tn (S10), and generates a histogram with weights added (S11).
- the three-dimensional object detection unit 33 calculates a relative movement distance that is a movement distance of the three-dimensional object with respect to the host vehicle V based on the histogram (S12). Next, the three-dimensional object detection unit 33 calculates the absolute movement speed of the three-dimensional object from the relative movement distance (S13). At this time, the three-dimensional object detection unit 33 calculates the relative movement speed by differentiating the relative movement distance with respect to time, and adds the own vehicle speed detected by the vehicle speed sensor 20 to calculate the absolute movement speed.
- the three-dimensional object detection unit 33 determines whether the absolute movement speed of the three-dimensional object is 10 km / h or more and the relative movement speed of the three-dimensional object with respect to the host vehicle V is +60 km / h or less (S14). When both are satisfied (S14: YES), the three-dimensional object detection unit 33 determines that the three-dimensional object is the other vehicle VX (S15). Then, the processes shown in FIGS. 11 and 12 are terminated. On the other hand, when either one is not satisfied (S14: NO), the three-dimensional object detection unit 33 determines that there is no other vehicle (S16). Then, the processes shown in FIGS. 11 and 12 are terminated.
- an example of performing a process of calculating a moving speed (movement distance) of a three-dimensional object after a process of determining whether or not a three-dimensional object exists in the detection region is shown, but this order is not particularly limited. That is, when the moving speed of the three-dimensional object is within the predetermined value range, the three-dimensional object detection process may be performed based on the difference waveform information. In addition, the three-dimensional object detection (presence / absence) process and the three-dimensional object movement speed calculation process may be performed in parallel.
- the rear side of the host vehicle V is set as the detection areas A1 and A2, and the vehicle V travels in the adjacent lane that travels next to the travel lane of the host vehicle to which attention should be paid while traveling.
- Emphasis is placed on detecting the vehicle VX, and in particular, whether or not there is a possibility of contact when the host vehicle V changes lanes. This is to determine whether or not there is a possibility of contact with another vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the own vehicle when the own vehicle V changes lanes. For this reason, the process of step S14 is performed.
- step S14 it is determined whether the absolute moving speed of the three-dimensional object is 10 km / h or more and the relative moving speed of the three-dimensional object with respect to the vehicle V is +60 km / h or less.
- the absolute moving speed of the stationary object may be detected to be several km / h. Therefore, by determining whether the speed is 10 km / h or more, it is possible to reduce the possibility of determining that the stationary object is the other vehicle VX.
- the relative speed of the three-dimensional object with respect to the host vehicle V may be detected at a speed exceeding +60 km / h. Therefore, the possibility of erroneous detection due to noise can be reduced by determining whether the relative speed is +60 km / h or less.
- step S14 it may be determined that the absolute movement speed is not negative or not 0 km / h. Further, in the present embodiment, since emphasis is placed on whether or not there is a possibility of contact when the host vehicle V changes lanes, when another vehicle VX is detected in step S15, the driver of the host vehicle is notified. A warning sound may be emitted or a display corresponding to a warning may be performed by a predetermined display device.
- the number of pixels indicating a predetermined difference is counted on the data of the difference image PD t along the direction in which the three-dimensional object falls by viewpoint conversion.
- the difference waveform DW t is generated by frequency distribution.
- the pixel indicating the predetermined difference on the data of the difference image PD t is a pixel that has changed in an image at a different time, in other words, a place where a three-dimensional object exists.
- the difference waveform DW t is generated by counting the number of pixels along the direction in which the three-dimensional object collapses and performing frequency distribution at the location where the three-dimensional object exists.
- the differential waveform DW t is generated from the information in the height direction for the three-dimensional object. Then, the moving distance of the three-dimensional object is calculated from the time change of the differential waveform DW t including the information in the height direction. For this reason, compared with the case where only one point of movement is focused on, the detection location before the time change and the detection location after the time change are specified including information in the height direction. The same location is likely to be obtained, and the movement distance is calculated from the time change of the same location, so that the calculation accuracy of the movement distance can be improved.
- the count number of the frequency distribution is set to zero for the portion corresponding to the smear S generation region in the differential waveform DW t .
- the waveform portion generated by the smear S in the differential waveform DW t is removed, and a situation in which the smear S is mistaken as a three-dimensional object can be prevented.
- the moving distance of the three-dimensional object is calculated from the offset amount of the differential waveform DW t when the error of the differential waveform DW t generated at different times is minimized. For this reason, the movement distance is calculated from the offset amount of the one-dimensional information called the waveform, and the calculation cost can be suppressed in calculating the movement distance.
- the differential waveform DW t generated at different times is divided into a plurality of small regions DW t1 to DW tn .
- a plurality of waveforms representing respective portions of the three-dimensional object are obtained.
- weighting is performed for each of the plurality of small areas DW t1 to DW tn , and the offset amount obtained for each of the small areas DW t1 to DW tn is counted according to the weight to form a histogram. For this reason, the moving distance can be calculated more appropriately by increasing the weight for the characteristic area and decreasing the weight for the non-characteristic area. Therefore, the calculation accuracy of the moving distance can be further improved.
- the weight is increased as the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference increases. For this reason, the characteristic undulation region having a large difference between the maximum value and the minimum value has a larger weight, and the flat region having a small undulation has a smaller weight.
- the moving distance is calculated by increasing the weight in the area where the difference between the maximum value and the minimum value is large. The accuracy can be further improved.
- the moving distance of the three-dimensional object is calculated from the maximum value of the histogram obtained by counting the offset amount obtained for each of the small areas DW t1 to DW tn . For this reason, even if there is a variation in the offset amount, a more accurate movement distance can be calculated from the maximum value.
- the offset amount for a stationary object is obtained and this offset amount is ignored, it is possible to prevent a situation in which the calculation accuracy of the moving distance of the three-dimensional object is lowered due to the stationary object.
- the calculation of the moving distance of the three-dimensional object is stopped. For this reason, it is possible to prevent a situation in which an erroneous movement distance having a plurality of maximum values is calculated.
- the vehicle speed of the host vehicle V is determined based on a signal from the vehicle speed sensor 20, but the present invention is not limited to this, and the speed may be estimated from a plurality of images at different times. In this case, a vehicle speed sensor becomes unnecessary, and the configuration can be simplified.
- the captured image at the current time and the image one hour before are converted into a bird's-eye view, the converted bird's-eye view is aligned, the difference image PD t is generated, and the generated difference image PD
- t is evaluated along the falling direction (the falling direction of the three-dimensional object when the captured image is converted into a bird's eye view)
- the differential waveform DW t is generated, but the present invention is not limited to this.
- the differential waveform DW t may be generated by evaluating along the direction corresponding to the falling direction (that is, the direction in which the falling direction is converted into the direction on the captured image).
- the difference image PD t is generated from the difference between the two images subjected to the alignment, and the difference image PD t is converted into a bird's eye view
- the bird's-eye view does not necessarily have to be clearly generated as long as the evaluation can be performed along the direction in which the user falls.
- FIGS. 13A and 13B are diagrams illustrating an imaging range and the like of the camera 10 in FIG. 3.
- FIG. 13A is a plan view
- FIG. 13B is a perspective view in real space on the rear side from the host vehicle V. Show.
- the camera 10 has a predetermined angle of view a, and images the rear side from the host vehicle V included in the predetermined angle of view a.
- the angle of view “a” of the camera 10 is set so that the imaging range of the camera 10 includes the adjacent lane in addition to the lane in which the host vehicle V travels.
- the detection areas A1 and A2 in this example are trapezoidal in a plan view (when viewed from a bird's eye), and the positions, sizes, and shapes of the detection areas A1 and A2 are determined based on the distances d 1 to d 4. Is done.
- the detection areas A1 and A2 in the example shown in the figure are not limited to a trapezoidal shape, and may be other shapes such as a rectangle when viewed from a bird's eye view as shown in FIG. Note that the detection area setting unit 34 in the present embodiment can also set the detection areas A1 and A2 by the method described above.
- the distance d1 is a distance from the host vehicle V to the ground lines L1 and L2.
- the ground lines L1 and L2 mean lines on which a three-dimensional object existing in the lane adjacent to the lane in which the host vehicle V travels contacts the ground.
- the purpose of the present embodiment is to detect other vehicles VX and the like (including two-wheeled vehicles) traveling in the left and right lanes adjacent to the lane of the host vehicle V on the rear side of the host vehicle V.
- a distance d1 which is a position to be the ground lines L1 and L2 of the other vehicle VX is obtained from a distance d11 from the own vehicle V to the white line W and a distance d12 from the white line W to a position where the other vehicle VX is predicted to travel. It can be determined substantially fixedly.
- the distance d1 is not limited to being fixedly determined, and may be variable.
- the computer 30 recognizes the position of the white line W with respect to the host vehicle V by a technique such as white line recognition, and determines the distance d11 based on the recognized position of the white line W.
- the distance d1 is variably set using the determined distance d11.
- the distance d1 is It shall be fixedly determined.
- the distance d2 is a distance extending from the rear end portion of the host vehicle V in the vehicle traveling direction.
- the distance d2 is determined so that the detection areas A1 and A2 are at least within the angle of view a of the camera 10.
- the distance d2 is set so as to be in contact with the range divided into the angle of view a.
- the distance d3 is a distance indicating the length of the detection areas A1, A2 in the vehicle traveling direction. This distance d3 is determined based on the size of the three-dimensional object to be detected. In the present embodiment, since the detection target is the other vehicle VX or the like, the distance d3 is set to a length including the other vehicle VX.
- the distance d4 is a distance indicating a height set so as to include a tire such as the other vehicle VX in the real space.
- the distance d4 is a length shown in FIG. 13A in the bird's-eye view image.
- the distance d4 may be a length that does not include a lane that is further adjacent to the left and right adjacent lanes in the bird's-eye view image (that is, a lane that is adjacent to two lanes).
- the distances d1 to d4 are determined, and thereby the positions, sizes, and shapes of the detection areas A1 and A2 are determined. More specifically, the position of the upper side b1 of the detection areas A1 and A2 forming a trapezoid is determined by the distance d1. The starting point position C1 of the upper side b1 is determined by the distance d2. The end point position C2 of the upper side b1 is determined by the distance d3. The side b2 of the detection areas A1 and A2 having a trapezoidal shape is determined by a straight line L3 extending from the camera 10 toward the starting point position C1.
- a side b3 of trapezoidal detection areas A1 and A2 is determined by a straight line L4 extending from the camera 10 toward the end position C2.
- the position of the lower side b4 of the detection areas A1 and A2 having a trapezoidal shape is determined by the distance d4.
- the areas surrounded by the sides b1 to b4 are set as the detection areas A1 and A2.
- the detection areas A ⁇ b> 1 and A ⁇ b> 2 are true squares (rectangles) in the real space behind the host vehicle V.
- the viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging with the camera 10.
- the viewpoint conversion unit 31 performs viewpoint conversion processing on the input captured image data to the bird's-eye image data in a bird's-eye view state.
- the bird's-eye view is a state seen from the viewpoint of a virtual camera looking down from above, for example, vertically downward (or slightly obliquely downward).
- This viewpoint conversion process can be realized by a technique described in, for example, Japanese Patent Application Laid-Open No. 2008-219063.
- the luminance difference calculation unit 35 calculates a luminance difference with respect to the bird's-eye view image data subjected to viewpoint conversion by the viewpoint conversion unit 31 in order to detect the edge of the three-dimensional object included in the bird's-eye view image. For each of a plurality of positions along a vertical imaginary line extending in the vertical direction in the real space, the brightness difference calculating unit 35 calculates a brightness difference between two pixels in the vicinity of each position.
- the luminance difference calculation unit 35 can calculate the luminance difference by either a method of setting only one vertical virtual line extending in the vertical direction in the real space or a method of setting two vertical virtual lines.
- the brightness difference calculation unit 35 applies a first vertical imaginary line corresponding to a line segment extending in the vertical direction in the real space and a vertical direction in the real space different from the first vertical imaginary line with respect to the bird's-eye view image that has undergone viewpoint conversion.
- a second vertical imaginary line corresponding to the extending line segment is set.
- the luminance difference calculation unit 35 continuously obtains a luminance difference between a point on the first vertical imaginary line and a point on the second vertical imaginary line along the first vertical imaginary line and the second vertical imaginary line.
- the operation of the luminance difference calculation unit 35 will be described in detail.
- the luminance difference calculation unit 35 corresponds to a line segment extending in the vertical direction in the real space and passes through the detection area A1 (hereinafter referred to as the attention line La).
- the luminance difference calculation unit 35 corresponds to a line segment extending in the vertical direction in the real space and also passes through the second vertical virtual line Lr (hereinafter referred to as a reference line Lr) passing through the detection area A1.
- the reference line Lr is set at a position separated from the attention line La by a predetermined distance in the real space.
- the line corresponding to the line segment extending in the vertical direction in the real space is a line that spreads radially from the position Ps of the camera 10 in the bird's-eye view image.
- This radially extending line is a line along the direction in which the three-dimensional object falls when converted to bird's-eye view.
- the luminance difference calculation unit 35 sets the attention point Pa (point on the first vertical imaginary line) on the attention line La.
- the luminance difference calculation unit 35 sets a reference point Pr (a point on the second vertical plate) on the reference line Lr.
- the attention line La, the attention point Pa, the reference line Lr, and the reference point Pr have the relationship shown in FIG. 14B in the real space.
- the attention line La and the reference line Lr are lines extending in the vertical direction in the real space, and the attention point Pa and the reference point Pr are substantially the same height in the real space. This is the point that is set.
- the attention point Pa and the reference point Pr do not necessarily have the same height, and an error that allows the attention point Pa and the reference point Pr to be regarded as the same height is allowed.
- the luminance difference calculation unit 35 obtains a luminance difference between the attention point Pa and the reference point Pr. If the luminance difference between the attention point Pa and the reference point Pr is large, it is considered that an edge exists between the attention point Pa and the reference point Pr. Therefore, the edge line detection unit 36 shown in FIG. 3 detects an edge line based on the luminance difference between the attention point Pa and the reference point Pr.
- FIG. 15 is a diagram illustrating a detailed operation of the luminance difference calculation unit 35, in which FIG. 15 (a) shows a bird's-eye view image in a bird's-eye view state, and FIG. 15 (b) is shown in FIG. 15 (a). It is the figure which expanded a part B1 of the bird's-eye view image. Although only the detection area A1 is illustrated and described in FIG. 15, the luminance difference is calculated in the same procedure for the detection area A2.
- the other vehicle VX When the other vehicle VX is reflected in the captured image captured by the camera 10, the other vehicle VX appears in the detection area A1 in the bird's-eye view image as shown in FIG. As shown in the enlarged view of the area B1 in FIG. 15A in FIG. 15B, it is assumed that the attention line La is set on the rubber part of the tire of the other vehicle VX on the bird's-eye view image.
- the luminance difference calculation unit 35 first sets the reference line Lr.
- the reference line Lr is set along the vertical direction at a position away from the attention line La by a predetermined distance in the real space.
- the reference line Lr is set at a position separated from the attention line La by 10 cm in real space.
- the reference line Lr is set on the wheel of the tire of the other vehicle VX that is separated from the rubber of the tire of the other vehicle VX by, for example, 10 cm on the bird's eye view image.
- the luminance difference calculation unit 35 sets a plurality of attention points Pa1 to PaN on the attention line La.
- attention point Pai when an arbitrary point is indicated
- the number of attention points Pa set on the attention line La may be arbitrary.
- N attention points Pa are set on the attention line La.
- the luminance difference calculation unit 35 sets the reference points Pr1 to PrN so as to be the same height as the attention points Pa1 to PaN in the real space. Then, the luminance difference calculation unit 35 calculates the luminance difference between the attention point Pa and the reference point Pr having the same height. Thereby, the luminance difference calculation unit 35 calculates the luminance difference between the two pixels for each of a plurality of positions (1 to N) along the vertical imaginary line extending in the vertical direction in the real space. For example, the luminance difference calculating unit 35 calculates a luminance difference between the first attention point Pa1 and the first reference point Pr1, and the second difference between the second attention point Pa2 and the second reference point Pr2. Will be calculated.
- the luminance difference calculation unit 35 continuously calculates the luminance difference along the attention line La and the reference line Lr. That is, the luminance difference calculation unit 35 sequentially obtains the luminance difference between the third to Nth attention points Pa3 to PaN and the third to Nth reference points Pr3 to PrN.
- the luminance difference calculation unit 35 repeatedly executes the above-described processing such as setting the reference line Lr, setting the attention point Pa and the reference point Pr, and calculating the luminance difference while shifting the attention line La in the detection area A1. That is, the luminance difference calculation unit 35 repeatedly executes the above processing while changing the positions of the attention line La and the reference line Lr by the same distance in the extending direction of the ground line L1 in the real space. For example, the luminance difference calculation unit 35 sets the reference line Lr as the reference line Lr in the previous processing, sets the reference line Lr for the attention line La, and sequentially obtains the luminance difference. It will be.
- the edge line detection unit 36 detects an edge line from the continuous luminance difference calculated by the luminance difference calculation unit 35.
- the first attention point Pa ⁇ b> 1 and the first reference point Pr ⁇ b> 1 are located in the same tire portion, and thus the luminance difference is small.
- the second to sixth attention points Pa2 to Pa6 are located in the rubber part of the tire, and the second to sixth reference points Pr2 to Pr6 are located in the wheel part of the tire. Therefore, the luminance difference between the second to sixth attention points Pa2 to Pa6 and the second to sixth reference points Pr2 to Pr6 becomes large. Therefore, the edge line detection unit 36 may detect that an edge line exists between the second to sixth attention points Pa2 to Pa6 and the second to sixth reference points Pr2 to Pr6 having a large luminance difference. it can.
- the edge line detection unit 36 firstly follows the following Equation 1 to determine the i-th attention point Pai (coordinate (xi, yi)) and the i-th reference point Pri (coordinate ( xi ′, yi ′)) and the i th attention point Pai are attributed.
- Equation 1 t represents a threshold value
- I (xi, yi) represents the luminance value of the i-th attention point Pai
- I (xi ′, yi ′) represents the luminance value of the i-th reference point Pri.
- the attribute s (xi, yi) of the attention point Pai is “1”.
- the attribute s (xi, yi) of the attention point Pai is “ ⁇ 1”.
- the edge line detection unit 36 determines whether or not the attention line La is an edge line from the continuity c (xi, yi) of the attribute s along the attention line La based on Equation 2 below.
- the continuity c (xi, yi) is “1”.
- the attribute s (xi, yi) of the attention point Pai is not the same as the attribute s (xi + 1, yi + 1) of the adjacent attention point Pai + 1
- the continuity c (xi, yi) is “0”.
- the edge line detection unit 36 obtains the sum for the continuity c of all the points of interest Pa on the line of interest La.
- the edge line detection unit 36 normalizes the continuity c by dividing the obtained sum of continuity c by the number N of points of interest Pa.
- the edge line detection unit 36 determines that the attention line La is an edge line when the normalized value exceeds the threshold ⁇ .
- the threshold value ⁇ is a value set in advance through experiments or the like.
- the edge line detection unit 36 determines whether or not the attention line La is an edge line based on Equation 3 below. Then, the edge line detection unit 36 determines whether or not all the attention lines La drawn on the detection area A1 are edge lines. [Equation 3] ⁇ c (xi, yi) / N> ⁇
- the three-dimensional object detection unit 37 detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 36.
- the three-dimensional object detection device 1 detects an edge line extending in the vertical direction in real space. The fact that many edge lines extending in the vertical direction are detected means that there is a high possibility that a three-dimensional object exists in the detection areas A1 and A2. For this reason, the three-dimensional object detection unit 37 detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 36. Furthermore, prior to detecting the three-dimensional object, the three-dimensional object detection unit 37 determines whether or not the edge line detected by the edge line detection unit 36 is correct.
- the three-dimensional object detection unit 37 determines whether or not the luminance change along the edge line of the bird's-eye view image on the edge line is larger than a predetermined threshold value. When the luminance change of the bird's-eye view image on the edge line is larger than the threshold value, it is determined that the edge line is detected by erroneous determination. On the other hand, when the luminance change of the bird's-eye view image on the edge line is not larger than the threshold value, it is determined that the edge line is correct.
- This threshold value is a value set in advance by experiments or the like.
- FIG. 16 is a diagram illustrating the luminance distribution of the edge line.
- FIG. 16A illustrates the edge line and the luminance distribution when another vehicle VX as a three-dimensional object exists in the detection area A1, and
- FIG. Indicates an edge line and a luminance distribution when there is no solid object in the detection area A1.
- the attention line La set in the tire rubber portion of the other vehicle VX is determined to be an edge line in the bird's-eye view image.
- the luminance change of the bird's-eye view image on the attention line La is gentle. This is because the tire of the other vehicle VX is extended in the bird's-eye view image by converting the image captured by the camera 10 into a bird's-eye view image.
- the attention line La set in the white character portion “50” drawn on the road surface in the bird's-eye view image is erroneously determined as an edge line.
- the brightness change of the bird's-eye view image on the attention line La has a large undulation. This is because a portion with high brightness in white characters and a portion with low brightness such as a road surface are mixed on the edge line.
- the three-dimensional object detection unit 37 determines whether or not the edge line is detected by erroneous determination. When the luminance change along the edge line is larger than a predetermined threshold, the three-dimensional object detection unit 37 determines that the edge line is detected by erroneous determination. And the said edge line is not used for the detection of a solid object. Thereby, white characters such as “50” on the road surface, weeds on the road shoulder, and the like are determined as edge lines, and the detection accuracy of the three-dimensional object is prevented from being lowered.
- the three-dimensional object detection unit 37 calculates the luminance change of the edge line by any one of the following mathematical formulas 4 and 5.
- the luminance change of the edge line corresponds to the evaluation value in the vertical direction in the real space.
- Equation 4 evaluates the luminance distribution by the sum of the squares of the differences between the i-th luminance value I (xi, yi) on the attention line La and the adjacent i + 1-th luminance value I (xi + 1, yi + 1).
- Equation 5 evaluates the luminance distribution by the sum of the absolute values of the differences between the i-th luminance value I (xi, yi) on the attention line La and the adjacent i + 1-th luminance value I (xi + 1, yi + 1).
- the attribute b (xi, yi) of the attention point Pa (xi, yi) is “1”. Become. If the relationship is other than that, the attribute b (xi, yi) of the attention point Pai is '0'.
- This threshold value t2 is set in advance by an experiment or the like in order to determine that the attention line La is not on the same three-dimensional object. Then, the three-dimensional object detection unit 37 sums up the attributes b for all the attention points Pa on the attention line La, obtains an evaluation value in the vertical equivalent direction, and determines whether the edge line is correct.
- 17 and 18 are flowcharts showing details of the three-dimensional object detection method according to the present embodiment.
- FIG. 17 and FIG. 18 for the sake of convenience, the processing for the detection area A1 will be described, but the same processing is executed for the detection area A2.
- step S20 the computer 30 sets a detection area based on a predetermined rule. This detection area setting method will be described in detail later.
- step S21 the camera 10 captures an image of a predetermined area specified by the angle of view a and the attachment position.
- step S22 the viewpoint conversion unit 31 inputs the captured image data captured by the camera 10 in step S21, performs viewpoint conversion, and generates bird's-eye view image data.
- step S23 the luminance difference calculation unit 35 sets the attention line La on the detection area A1. At this time, the luminance difference calculation unit 35 sets a line corresponding to a line extending in the vertical direction in the real space as the attention line La.
- luminance difference calculation part 35 sets the reference line Lr on detection area
- step S25 the luminance difference calculation unit 35 sets a plurality of attention points Pa on the attention line La.
- the luminance difference calculation unit 35 sets the attention points Pa as many as not causing a problem at the time of edge detection by the edge line detection unit 36.
- step S26 the luminance difference calculation unit 35 sets the reference point Pr so that the attention point Pa and the reference point Pr are substantially the same height in the real space. Thereby, the attention point Pa and the reference point Pr are arranged in a substantially horizontal direction, and it becomes easy to detect an edge line extending in the vertical direction in the real space.
- step S27 the luminance difference calculation unit 35 calculates the luminance difference between the attention point Pa and the reference point Pr that have the same height in the real space.
- the edge line detection unit 36 calculates the attribute s of each attention point Pa in accordance with Equation 1 above.
- step S28 the edge line detection unit 36 calculates the continuity c of the attribute s of each attention point Pa in accordance with Equation 2 above.
- step S29 the edge line detection unit 36 determines whether or not the value obtained by normalizing the total sum of continuity c is greater than the threshold value ⁇ according to the above formula 3.
- the edge line detection unit 36 detects the attention line La as an edge line in step S30. Then, the process proceeds to step S31.
- the edge line detection unit 36 does not detect the attention line La as an edge line, and the process proceeds to step S31.
- step S31 the computer 30 determines whether or not the processing in steps S23 to S30 has been executed for all the attention lines La that can be set on the detection area A1. If it is determined that the above processing has not been performed for all the attention lines La (S31: NO), the processing returns to step S23, a new attention line La is set, and the processing up to step S31 is repeated. On the other hand, when it is determined that the above process has been performed for all the attention lines La (S31: YES), the process proceeds to step S32 in FIG.
- step S32 of FIG. 18 the three-dimensional object detection unit 37 calculates a luminance change along the edge line for each edge line detected in step S30 of FIG.
- the three-dimensional object detection unit 37 calculates the luminance change of the edge line according to any one of the above formulas 4, 5, and 6.
- step S33 the three-dimensional object detection unit 37 excludes edge lines whose luminance change is larger than a predetermined threshold from the edge lines. That is, it is determined that an edge line having a large luminance change is not a correct edge line, and the edge line is not used for detecting a three-dimensional object. As described above, this is to prevent characters on the road surface, roadside weeds, and the like included in the detection area A1 from being detected as edge lines. Therefore, the predetermined threshold value is a value set based on a luminance change generated by characters on the road surface, weeds on the road shoulder, or the like obtained in advance by experiments or the like.
- step S34 the three-dimensional object detection unit 37 determines whether or not the amount of the edge line is equal to or larger than the second threshold value ⁇ .
- the second threshold value ⁇ is set based on the number of edge lines of the four-wheeled vehicle that have appeared in the detection region A1 in advance through experiments or the like.
- the three-dimensional object detection unit 37 detects that a three-dimensional object exists in the detection area A1 in step S35.
- the three-dimensional object detection unit 37 determines that there is no three-dimensional object in the detection area A1 (S38).
- the detected three-dimensional object may be determined to be another vehicle VX that travels in the adjacent lane adjacent to the lane in which the host vehicle V travels, and the relative speed of the detected three-dimensional object with respect to the host vehicle V is taken into consideration. It may be determined whether the vehicle is another vehicle VX traveling in the adjacent lane. Specifically, when a three-dimensional object is detected in step S35, the process proceeds to step S36, and it is determined whether or not the moving speed of the three-dimensional object is within a predetermined value range.
- step S35 it is determined whether or not the three-dimensional object detected in step S35 is another vehicle traveling in the adjacent lane.
- the moving speed of the three-dimensional object can be obtained based on the moving speed of the edge on the image.
- step S36 if the moving speed of the three-dimensional object detected in step S35 is within a predetermined value, the process proceeds to step S37, and the three-dimensional object is determined as another vehicle.
- the process proceeds to step S39, and it is determined that the three-dimensional object is not another vehicle.
- the predetermined range of the moving speed when determining whether or not the three-dimensional object is another vehicle is that the absolute moving speed is 10 km / h or more and the own vehicle of the three-dimensional object as described in the processing of FIG.
- the relative movement speed with respect to V can be +60 km / h or less.
- the predetermined value range of the moving speed it may be defined that the absolute moving speed is a positive value (not a negative value) or not 0 km / h. Thereafter, the processing illustrated in FIGS. 17 and 18 ends.
- an example of performing a process of calculating a moving speed (movement distance) of a three-dimensional object after a process of determining whether or not a three-dimensional object exists in the detection region is shown, but this order is not particularly limited. That is, when the moving speed of the three-dimensional object is within the predetermined value range, the three-dimensional object detection process may be performed based on the edge information. In addition, the three-dimensional object detection (presence / absence) process and the three-dimensional object movement speed calculation process may be performed simultaneously in parallel.
- the vertical direction in the real space with respect to the bird's-eye view image A vertical imaginary line is set as a line segment extending to. Then, for each of a plurality of positions along the vertical imaginary line, a luminance difference between two pixels in the vicinity of each position can be calculated, and the presence or absence of a three-dimensional object can be determined based on the continuity of the luminance difference.
- the attention line La corresponding to the line segment extending in the vertical direction in the real space and the reference line Lr different from the attention line La are set for the detection areas A1 and A2 in the bird's-eye view image. Then, a luminance difference between the attention point Pa on the attention line La and the reference point Pr on the reference line Lr is continuously obtained along the attention line La and the reference line La. In this way, the luminance difference between the attention line La and the reference line Lr is obtained by continuously obtaining the luminance difference between the points. In the case where the luminance difference between the attention line La and the reference line Lr is high, there is a high possibility that there is an edge of the three-dimensional object at the set position of the attention line La.
- a three-dimensional object can be detected based on a continuous luminance difference.
- the detection accuracy of a three-dimensional object can be improved.
- the luminance difference between two points of approximately the same height near the vertical imaginary line is obtained.
- the luminance difference is obtained from the attention point Pa on the attention line La and the reference point Pr on the reference line Lr, which are substantially the same height in the real space, and thus the luminance when there is an edge extending in the vertical direction. The difference can be detected clearly.
- FIG. 19 is a diagram illustrating an example of an image for explaining the processing of the edge line detection unit 36.
- 102 is an adjacent image.
- a region where the brightness of the first striped pattern 101 is high and a region where the brightness of the second striped pattern 102 is low are adjacent to each other, and a region where the brightness of the first striped pattern 101 is low and the second striped pattern 102. Is adjacent to a region with high brightness.
- the portion 103 located at the boundary between the first striped pattern 101 and the second striped pattern 102 tends not to be perceived as an edge depending on human senses.
- the edge line detection unit 36 determines the part 103 as an edge line only when there is continuity in the attribute of the luminance difference in addition to the luminance difference in the part 103, the edge line detection unit 36 An erroneous determination of recognizing a part 103 that is not recognized as an edge line as a sensation as an edge line can be suppressed, and edge detection according to a human sensation can be performed.
- the edge line detection unit 36 when the luminance change of the edge line detected by the edge line detection unit 36 is larger than a predetermined threshold value, it is determined that the edge line has been detected by erroneous determination.
- the captured image acquired by the camera 10 is converted into a bird's-eye view image, the three-dimensional object included in the captured image tends to appear in the bird's-eye view image in a stretched state.
- the luminance change of the bird's-eye view image in the stretched direction tends to be small.
- the bird's-eye view image includes a high luminance region such as a character portion and a low luminance region such as a road surface portion.
- the brightness change in the stretched direction tends to increase in the bird's-eye view image. Therefore, by determining the luminance change of the bird's-eye view image along the edge line as in this example, the edge line detected by the erroneous determination can be recognized, and the detection accuracy of the three-dimensional object can be improved.
- the three-dimensional object detection units 33 and 37 send detection results to an external vehicle controller for further notification to the occupant and vehicle control.
- the three-dimensional object detection device 1 of the present example is a three-dimensional object detected by the above-described two three-dimensional object detection units 33 (or the three-dimensional object detection unit 37), and the three-dimensional object is a detection target.
- a determination unit that finally determines whether the vehicle is a vehicle VX can also be provided.
- the detection region setting unit 34 includes the first detection region and the second detection region having different areas for the detection regions A1 and A2. Specifically, the detection region setting unit 34 sets the first detection regions A11 and A21 when the three-dimensional object detection units 33 and 37 calculate the movement distance of the three-dimensional object existing in the detection regions A1 and A2, and the three-dimensional object detection unit 33 and 37 sets the first detection regions A11 and A21. When the object detection units 33 and 37 detect solid objects existing in the detection areas A1 and A2, the second detection areas A12 and A22 having a smaller area than the first detection areas A11 and A21 are set.
- the detection area setting unit 34 has a predetermined first detection area and a distance in the lateral direction of the vehicle from the installation position of the camera 10 in the first detection area for each of the detection areas A1 and A2. And a second detection region in which a band-like region outside the first detection region (on the shoulder side) is lost.
- the second detection area is an area in which the roadside side of the first detection area is moved to the vehicle side and the width of the detection area is narrowed in the vehicle width direction.
- the first detection area of the present embodiment may be defined as an area common to the default detection area. The mode of this detection area will be described later with reference to FIG.
- the three-dimensional object detection device 1 detects the presence of a three-dimensional object and the moving speed based on image information of a captured image behind the vehicle acquired by the in-vehicle camera 10.
- an image of an object such as a planting or guardrail provided on the shoulder of the traveling lane of the host vehicle is erroneously displayed in the adjacent lane.
- an image of an object such as a planting or guardrail provided on the shoulder of the traveling lane of the host vehicle is erroneously displayed in the adjacent lane.
- it is mistaken as an image of another vehicle traveling on the road. As shown in FIG.
- the guard rail Q2 or curb stone Q2 having the same shape on the shoulder
- the image information includes feature points like the same object even though it is a different object when periodically photographed by the camera 10. May end up.
- the shape of the planting / grass Q1 is not constant, the feature points included in the image information may accidentally resemble the feature points of the other vehicle VX.
- the guardrail / curbstone Q2 since the guard rail / curbstone Q2 having the same shape is repeatedly arranged, the guardrail / curbstone differs depending on the combination of the imaging period of the camera 10, the vehicle speed of the host vehicle V, and the unit length of the repeated pattern of the guardrail / curbstone Q2.
- the image information of Q2 may indicate a feature point like the same object, or the feature point included in the image information of the guardrail / curbstone Q2 may accidentally resemble the feature point of the other vehicle VX.
- the objects other than the detection target such as the planting / grass Q1 or the guardrail / curbstone Q2 provided on the shoulder of the traveling lane of the own vehicle V
- the video is mistakenly mistaken as the video of the other vehicle VX traveling in the adjacent lane.
- the traveling lane of the host vehicle is narrow, the image of the object on the road shoulder enters the region corresponding to the adjacent lane, and thus the above problem is likely to occur.
- the viewpoint of the image is converted into a bird's-eye view image, the image spreads horizontally and the image of the object on the road shoulder enters the detection areas A1 and A2, and the above problem is likely to occur.
- the three-dimensional object detection device 1 prepares two detection areas A11, A12, A21, and A22 having different areas for each of the detection areas A1 and A2, and the detection areas according to the processing characteristics. Switch.
- the detection area setting unit 34 sets the relatively wide first detection areas A11 and A21 when the three-dimensional object detection units 33 and 37 calculate the movement distance of the three-dimensional object, and the three-dimensional object detection unit 33. , 37 detect the three-dimensional object existing in the detection areas A1 and A2, the second detection areas A12 and A22 having a smaller area than the first detection areas A11 and A21 are set.
- the host vehicle V is moving and solid objects (including other vehicles VX) may also move, the calculation of the speed (movement distance per hour) of the three-dimensional object is not expected to be performed over a wide range.
- the detection result cannot be obtained.
- the detection areas A1 and A2 when detecting the moving speed of the three-dimensional object need to set a distance of a predetermined value or more along the traveling direction of the host vehicle V.
- the detection areas A1 and A2 it is necessary to set the detection areas A1 and A2 to be narrow in order to eliminate the influence when the image information includes images of roadside shoulder planting, grass, guardrails, curbs, and the like.
- the detection areas A1 and A2 when detecting the moving speed of the three-dimensional object are set to a distance less than a predetermined value along the traveling direction of the host vehicle V, or the detection areas A1 and A2 are in the vehicle width direction of the host vehicle V. It is preferable that the distance is less than a predetermined value along.
- first detection areas A11, A21 and second detection areas A12, A22 By setting these two first detection areas A11, A21 and second detection areas A12, A22 according to the processing scene, while accurately grasping the moving speed of the three-dimensional object, planting the road shoulder, grass,
- the other vehicle VX can be detected with high accuracy without erroneously detecting a guardrail, a curbstone, or the like as the other vehicle VX.
- the first detection region A11 and the second detection region A12, or the first detection region A21 and the second detection region A22 may have the same area and the same form, or may have different areas and different forms. Good.
- the detection area A1 of the present embodiment has a first detection area A11 and a second detection area A12.
- the detection area A1 will be described as an example, but the same applies to the first detection area A21 and the second detection area A22 of the detection area A2.
- the rear distance from the camera 10 is a distance along the + y direction in the vehicle length direction, and the lateral distance from the camera 10 is along the + x in the vehicle width direction. Distance.
- the detection areas A11, A12, A21, and A22 are one aspect of the detection area set by the detection area setting unit 34, and are not particularly limited as long as they are set on the right side and the left side behind the vehicle.
- the detection area setting unit 34 of the three-dimensional object detection device may set the first detection areas A11 and A21 and the second detection areas A21 and A22 that are the same as the predetermined detection area set by default, for example, set by default.
- the first detection areas A11 and A21 and the second detection areas A21 and A22 included in the predetermined detection area may be set.
- the area A13 in which the rear distance (the distance in the + y direction) from the position of the camera 10 in the first detection area 11 is a predetermined distance or more is lost ( (Removed) area.
- the length D2 of the second detection area A12 along the traveling direction of the host vehicle V is shorter than the length D1 of the first detection area A11.
- the relationship between the lengths D2 and D1 can be appropriately set according to the size of the detection areas A1 and A2.
- the second detection region A12 as a region in which the rear distance from the camera 10 is a predetermined distance or more in the first detection region A11, planting of the road shoulder, grass, guardrail, It is difficult to extract periodic features from images such as curbs, the probability that feature points like the same object will be included in the image information, and the feature points included in the image information are the feature points of the other vehicle VX. The probability of being similar can be reduced.
- the second detection area A12 of this example is an area A13 in which the lateral distance (distance in the ⁇ x direction) from the position of the camera 10 in the first detection area A11 is a predetermined distance or more. This is a region where is missing. That is, the second detection areas A12 and A21 are areas where the areas outside the first detection areas A11 and A21 (the side away from the vehicle) are excluded. As shown in the figure, the length W2 of the second detection area A12 along the vehicle width direction of the host vehicle V is shorter than the length W1 of the first detection area A11. The relationship between the lengths W2 and W1 can be appropriately set according to the sizes of the detection areas A1 and A2. In this aspect, as shown in FIG.
- the predetermined lateral distance from the position of the camera 10 can gradually increase from W3 to W2, and further to W1, as the distance from the camera 10 increases.
- the second detection region A12 as a region in which the side distance from the camera 10 in the first detection region 11 is greater than or equal to a predetermined distance, planting roadsides, grass, guardrails, Since it is possible to mask images such as curbs so that they are not included in the image information used for detection processing, whether or not they are solid objects such as roadside planting, grass, guardrails, curbs, etc. It can be made not to judge.
- the rear distance from the camera 10 in the first detection area 11 is within a predetermined distance, and the lateral distance from the camera 10 is greater than or equal to the predetermined distance.
- the length D2 of the second detection region A12 along the traveling direction of the host vehicle V is shorter than the length D1 of the first detection region A11 and the second detection region A12 extends along the vehicle width direction of the host vehicle V.
- the length W2 of the second detection area A12 is shorter than the length W1 of the first detection area A11. D1.
- the relationship between D2, W1, and W2 is not particularly limited, and can be set as appropriate according to the sizes of the detection areas A1 and A2. Further, in this aspect, the area of the region A13 to be deleted is not particularly limited as long as the vertex E1 on the outer front side of the first detection region A11 is included, and the distance between W2 and D2 can be arbitrarily set.
- the second detection area A22 set in the detection area A2 on the right side behind the vehicle shown in FIG. 24 is located at the right end on the vehicle front side in the first detection area A21 set in the same detection area A2.
- the second detection area A12 set in the detection area A1 on the left side of the rear of the vehicle is an area in which the area A23 including the vertex E2 that is positioned is lost, and is the first detection area A11 set in the same detection area A1
- the region A13 to be deleted in the first detection region A11 may include at least the vertex E1
- the A23 to be deleted in the first detection region A21 may include at least the vertex E2.
- the other vehicle VX can be detected with high accuracy without erroneously detecting road shoulder planting, grass, guardrail, curbstone, etc. as the other vehicle VX. Can do.
- the detection area setting section 34 can greatly expand the areas of the second detection areas A12 and A22.
- the detection area setting unit 34 deletes the area A13 including the vertex E1 and the area A23 including the vertex E2, the areas of the second detection areas A12 and A22 are determined as the first detection area.
- the area can be the same as A11 and A21.
- FIG. 25 shows a control procedure when this processing is performed.
- the process illustrated in FIG. 25 is the current three-dimensional detection process performed using the result of the previous process after the previous three-dimensional object detection process. That is, as described above, the detection area setting unit 34 of the present embodiment detects the presence of a three-dimensional object using the image information of the first detection areas A11 and A21 when detecting the moving speed of the three-dimensional object. At this time, each detection area is set so as to use the image information of the second detection areas A12 and A22.
- FIG. 26 shows the second detection areas A12 and A22 in the normal state.
- step S46 the second detection areas A12 and A22 having a relatively small area are expanded.
- FIG. 27 shows the expanded second detection areas A12 ′ and A22 ′.
- the rear distance from the camera 10 of the second detection areas A12 and A22 is extended from D21 to D21 ′.
- the second detection areas A12 and A22 are extended along the traveling direction of the vehicle (the rear distance of the camera 10), but the second detection area is small in the vehicle width direction as in the example shown in FIG.
- the second detection areas A12 and A22 may be expanded by extending the distance W in the vehicle width direction.
- the detection area setting unit 34 sets the first detection areas A11 and A21 and the extended second detection areas A12 and A22.
- the three-dimensional object other vehicle VX
- priority is given to preventing false detection of planting and guardrails as other vehicles, but in the enhanced monitoring state where other vehicles are approaching, false detection Priority is given to continuously detecting other vehicles over prevention.
- the expanded second detection areas A12 and A22 are set, so that the three-dimensional object can be detected carefully and the accuracy of detection of the other vehicle VX is determined. Can be increased.
- step S43 to perform a solid object detection process.
- This three-dimensional object detection processing is performed according to the above-described processing using the difference waveform information of FIGS. 11 and 12 by the three-dimensional object detection unit 33 or the processing using the edge information of FIGS. 17 and 18 by the three-dimensional object detection unit 37. Is called.
- step 43 if a three-dimensional object is detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S45, and it is determined that the detected three-dimensional object is the other vehicle VX.
- step S47 On the other hand, when a solid object is not detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S47, and it is determined that no other vehicle VX exists in the detection areas A1 and A2.
- FIG. 28 is a flowchart showing another control procedure.
- the process illustrated in FIG. 28 is a process of setting a detection area using the calculation result of the moving speed of the three-dimensional object.
- the movement speed of the three-dimensional object may be performed using the result of the previous three-dimensional object detection process, or may be performed using the result of the movement speed calculation process performed in parallel with the three-dimensional object detection process.
- the detection area setting unit 34 of the present embodiment uses the image information of the first detection areas A11 and A21 when detecting the moving speed of the three-dimensional object, and the second detection area when detecting the presence of the three-dimensional object.
- the image information of A12 and A22 is used.
- FIG. 22 described above shows the second detection areas A12 and A22 and the first detection areas A11 and A21 in the normal state.
- step S51 If it is determined in step S51 that the moving speed of the three-dimensional object calculated based on the moving distance is within the predetermined threshold and it is highly likely that the three-dimensional object is another vehicle, the process proceeds to step S46.
- step S46 the second detection areas A12 and A22 whose widths are narrowed are expanded. Specifically, when the moving speed of the three-dimensional object is other than a predetermined value, it can be determined that the three-dimensional object is not another vehicle. For this reason, in this embodiment, in order to give priority to improving the accuracy of detection of a three-dimensional object, the second detection areas A12 and A22 are arranged as cameras in the first detection areas A11 and A21 as shown in FIG.
- the second detection areas A12 and A22 have a predetermined distance W2 + q (> W2) in the lateral direction W of the vehicle from the installation position of the camera 10 in the first detection areas A11 and A21.
- the above region is a missing region. That is, the area to be deleted is reduced and the second detection areas A12 and A22 are expanded.
- the second detection region in this case may have the same area and shape as the first detection regions A11 and A21.
- the detection area setting unit 34 sets the first detection areas A11, A21 and the extended second detection areas A12, A22.
- the moving speed of the three-dimensional object is outside the predetermined value range, it is considered that the three-dimensional object is unlikely to be another vehicle, so priority is given to preventing erroneous detection of planting or guardrail as another vehicle.
- the moving speed of the three-dimensional object is within the predetermined range, it is considered that the three-dimensional object is likely to be another vehicle. Therefore, it is preferable to perform the detection process with the maximum detection range.
- the accurate detection result of the already detected other vehicle (three-dimensional object) is continuously acquired rather than prevention of erroneous detection.
- the second detection areas A12 and A22 in which the road-side belt-like area is initially lost are expanded. Therefore, the other vehicle (three-dimensional object) once detected can be monitored with high accuracy while preventing the three-dimensional object from being erroneously detected due to the influence of the roadside structure.
- step S43 to perform a solid object detection process.
- This three-dimensional object detection processing is performed according to the above-described processing using the difference waveform information of FIGS. 11 and 12 by the three-dimensional object detection unit 33 or the processing using the edge information of FIGS. 17 and 18 by the three-dimensional object detection unit 37. Is called.
- step 43 if a three-dimensional object is detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S45, and it is determined that the detected three-dimensional object is the other vehicle VX.
- step S47 On the other hand, when a solid object is not detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S47, and it is determined that no other vehicle VX exists in the detection areas A1 and A2.
- the three-dimensional object detection device 1 of the present embodiment when calculating the movement distance of the three-dimensional object, the relatively wide first detection areas A11 and A21 are set, and the presence of the three-dimensional object Since the second detection areas A12 and A22 that are relatively narrow are set when the vehicle is detected, it is possible to prevent an object on the road shoulder of the traveling lane of the host vehicle from being included in the detection area and at a predetermined cycle along the road shoulder. The possibility that the feature of the appearing object is mistaken as the feature of the other vehicle VX in the adjacent lane can be reduced.
- the other vehicle VX can be detected with high accuracy without erroneously detecting a guardrail, a curbstone, or the like as the other vehicle VX.
- the second detection region A12 is a region in which the region A13 whose back distance from the camera 10 is greater than or equal to a predetermined distance in the first detection region 11 is lost. This makes it difficult to extract periodic features from images of roadside planting, grass, guardrails, curbs, etc., and the probability that feature points like the same object will be included in the image information, and image information It is possible to reduce the probability that the feature points included are similar to the feature points of the other vehicle VX. As a result, it is possible to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle. Other vehicles traveling in the lane can be detected with high accuracy.
- the second detection area A12 is defined as an area in which the area A13 whose lateral distance from the camera 10 is greater than or equal to a predetermined distance in the first detection area 11 is lost.
- images of roadside planting, grass, guardrail, curb, etc. can be excluded from the image information used for detection processing, so in the first place, roadside planting, grass, guardrail, curb, etc. It is possible not to determine whether or not it is a three-dimensional object.
- the second detection areas A12 and A22 are missing the area E1 including the vertex located at the right end on the vehicle front side of the first detection areas A11 and A21.
- the second detection areas A12, A22 are the first detection areas A11. Since the region including the vertex E2 located at the left end of the vehicle front side in A21 is missing, the same effect as the above (2) and (3) is obtained, and the moving speed of the three-dimensional object is accurately grasped. However, it is possible to detect the other vehicle VX with high accuracy without erroneously detecting planting of a road shoulder, grass, guardrail, curbstone, and the like as the other vehicle VX.
- the first detection area set by the detection area setting unit 34 when calculating the movement distance of the three-dimensional object is similarly set by the detection area setting unit 34 on each of the right and left sides behind the vehicle. For example, by making the detection area the same size as the default detection area, the calculation load can be reduced and a stable detection result can be derived.
- the expanded second detection areas A12 and A22 are set, so that the three-dimensional object is carefully detected. And the accuracy of detection of the other vehicle VX can be increased.
- the three-dimensional object detection process is performed based on the image information of the second detection region in which the roadside region is missing, and the movement distance (movement speed of the three-dimensional object) ) Is calculated based on the image information of the first detection area having no defect, and based on the moving speed of the three-dimensional object, it is determined that the three-dimensional object is likely to be another vehicle.
- the missing part of the second detection area is reduced, that is, the second detection area is expanded to the roadside.
- the camera 10 corresponds to an imaging unit according to the present invention
- the viewpoint conversion unit 31 corresponds to an image conversion unit according to the present invention
- the alignment unit 32 and the three-dimensional object detection unit 33 include a three-dimensional object detection according to the present invention.
- the luminance difference calculation unit 35, the edge line detection unit 36, and the three-dimensional object detection unit 37 correspond to a three-dimensional object detection unit according to the present invention
- the detection region setting unit 34 corresponds to a detection region setting unit.
- the vehicle speed sensor 20 corresponds to a vehicle speed sensor.
- SYMBOLS 1 Three-dimensional object detection apparatus 10 ... Camera 20 ... Vehicle speed sensor 30 ... Computer 31 ... Viewpoint conversion part 32 ... Position alignment part 33, 37 ... Three-dimensional object detection part 34 ... Detection area setting part 35 ... Luminance difference calculation part 36 ... Edge detection Section 50 ... Steering angle sensor 40 ... Smear detection section a ... Angle of view A1, A2 ... Detection area CP ... Intersection DP ... Difference pixels DW t , DW t '... Difference waveforms DW t1 to DW m , DW m + k to DW tn ... Small regions L1, L2 ... ground line La, Lb ...
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Abstract
The present invention is provided with: a detection-region setting unit (34) for setting a detection region for the right side and the left side to the rear of a vehicle; and a solid-object detection unit (33, 37) for detecting a solid object existing in a right-side detection region (A1) or a left-side detection region (A2) to the rear of a vehicle, on the basis of image information about the rear side of the vehicle from a camera (10). The detection-region setting unit (34) sets a first detection region (A11, A21) when the solid-object detection unit (33, 37) calculates the distance a solid object moves, and sets a second detection region (A12, A22) of smaller area than the first detection region (A11, A21) when the solid-body detection unit (33, 37) detects a solid body existing in the detection region (A1, A2).
Description
本発明は、立体物検出装置及び立体物検出方法に関するものである。
本出願は、2012年2月22日に出願された日本国特許出願の特願2012―036300に基づく優先権を主張するものであり、文献の参照による組み込みが認められる指定国については、上記の出願に記載された内容を参照により本出願に組み込み、本出願の記載の一部とする。 The present invention relates to a three-dimensional object detection device and a three-dimensional object detection method.
This application claims priority based on Japanese Patent Application No. 2012-036300 filed on Feb. 22, 2012. For the designated countries that are allowed to be incorporated by reference, The contents described in the application are incorporated into the present application by reference and made a part of the description of the present application.
本出願は、2012年2月22日に出願された日本国特許出願の特願2012―036300に基づく優先権を主張するものであり、文献の参照による組み込みが認められる指定国については、上記の出願に記載された内容を参照により本出願に組み込み、本出願の記載の一部とする。 The present invention relates to a three-dimensional object detection device and a three-dimensional object detection method.
This application claims priority based on Japanese Patent Application No. 2012-036300 filed on Feb. 22, 2012. For the designated countries that are allowed to be incorporated by reference, The contents described in the application are incorporated into the present application by reference and made a part of the description of the present application.
車両周囲を撮像した画像を俯瞰変換し、時間的に異なる二つの俯瞰変換画像の差分を用いて障害物を検出する障害物検出装置が知られている(特許文献1参照)。
2. Description of the Related Art An obstacle detection apparatus is known that detects an obstacle by performing overhead conversion of an image obtained by capturing an image of the surroundings of a vehicle and using a difference between two overhead conversion images that are temporally different (see Patent Document 1).
車両後方を撮像した画像を用いて自車両の走行車線の隣の隣接車線を走行する他車両を障害物として検出する際に、自車両の走行車線の路肩に設けられた植え込みやガードレール等の物体の映像を誤って隣接車線を走行する他車両の映像として誤認するという問題がある。特に、自車両の走行車線が狭い場合には、路肩に配置された物体の像が隣接車線に対応する領域に入ってしまうため、上記問題が起きやすい。
When detecting another vehicle traveling in the adjacent lane next to the traveling lane of the own vehicle as an obstacle using an image obtained by imaging the rear of the vehicle, an object such as an implantation or guardrail provided on the shoulder of the traveling lane of the own vehicle Is erroneously recognized as an image of another vehicle traveling in the adjacent lane. In particular, when the traveling lane of the host vehicle is narrow, an image of an object placed on the road shoulder enters an area corresponding to the adjacent lane, and thus the above problem is likely to occur.
本発明が解決しようとする課題は、自車両の走行車線の路肩の物体を、自車両の走行車線の隣の隣接車線を走行する他車両として誤検出することを防止して、隣接車線を走行する他車両を高い精度で検出することができる立体物検出装置を提供することである。
The problem to be solved by the present invention is to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle, and traveling in the adjacent lane. Another object of the present invention is to provide a three-dimensional object detection device that can detect other vehicles with high accuracy.
本発明は、立体物の移動距離を算出する場合には第1検出領域を設定し、検出領域に存在する立体物を検出する場合には第1検出領域よりも面積が小さい第2検出領域を設定することにより、上記課題を解決する。
The present invention sets a first detection area when calculating the movement distance of a three-dimensional object, and a second detection area having a smaller area than the first detection area when detecting a three-dimensional object existing in the detection area. By setting, the above-mentioned problem is solved.
本発明は、立体物の移動距離を算出する場合には相対的に広い第1検出領域を設定し、立体物の存在を検出する場合には相対的に狭い第2検出領域を設定するので、自車両の走行車線の路肩の物体が検出領域に含まれることを防止するとともに、路肩に沿って所定周期で現れる物体の特徴が隣接車線の他車両の特徴として誤認される可能性を低くすることにより、自車両の走行車線の路肩の物体を自車両の走行車線の隣の隣接車線を走行する他車両として誤検出することを防止することができる。この結果、自車両の走行車線の隣の隣接車線を走行する他車両を、高い精度で検出する立体物検出装置を提供することができる。
Since the present invention sets a relatively wide first detection area when calculating the moving distance of a three-dimensional object, and sets a relatively narrow second detection area when detecting the presence of a three-dimensional object, To prevent the object on the shoulder of the driving lane of the host vehicle from being included in the detection area, and to reduce the possibility that the feature of the object appearing in a predetermined cycle along the shoulder is misidentified as the feature of the other vehicle in the adjacent lane Accordingly, it is possible to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle. As a result, it is possible to provide a three-dimensional object detection device that detects, with high accuracy, other vehicles that travel in the adjacent lane adjacent to the travel lane of the host vehicle.
図1は、本発明の立体物検出装置1を適用した一実施の形態に係る車両の概略構成図であり、本例の立体物検出装置1は、自車両Vの運転者が運転中に注意を払うべき他車両、例えば、自車両Vが車線変更する際に接触の可能性がある他車両を障害物として検出する装置である。特に、本例の立体物検出装置1は自車両が走行する車線の隣の隣接車線(以下、単に隣接車線ともいう)を走行する他車両を検出する。また、本例の立体物検出装置1は、検出した他車両の移動距離、移動速度を算出することができる。このため、以下説明する一例は、立体物検出装置1を自車両Vに搭載し、自車両周囲において検出される立体物のうち、自車両Vが走行する車線の隣の隣接車線を走行する他車両を検出する例を示すこととする。同図に示すように、本例の立体物検出装置1は、カメラ10と、車速センサ20と、計算機30とを備える。
FIG. 1 is a schematic configuration diagram of a vehicle according to an embodiment to which a three-dimensional object detection device 1 of the present invention is applied. The three-dimensional object detection device 1 of the present example is careful when the driver of the host vehicle V is driving. Is a device that detects, as an obstacle, other vehicles that are likely to be contacted, for example, other vehicles that may be contacted when the host vehicle V changes lanes. In particular, the three-dimensional object detection device 1 of this example detects another vehicle that travels in an adjacent lane (hereinafter also simply referred to as an adjacent lane) adjacent to the lane in which the host vehicle travels. Further, the three-dimensional object detection device 1 of the present example can calculate the detected movement distance and movement speed of the other vehicle. For this reason, in the example described below, the three-dimensional object detection device 1 is mounted on the own vehicle V, and the three-dimensional object detected around the own vehicle travels in the adjacent lane next to the lane on which the own vehicle V travels. An example of detecting a vehicle will be shown. As shown in the figure, the three-dimensional object detection device 1 of the present example includes a camera 10, a vehicle speed sensor 20, and a calculator 30.
カメラ10は、図1に示すように自車両Vの後方における高さhの箇所において、光軸が水平から下向きに角度θとなるように自車両Vに取り付けられている。カメラ10は、この位置から自車両Vの周囲環境のうちの所定領域を撮像する。本実施形態において自車両Vの後方の立体物を検出するために設けられるカメラ1は一つであるが、他の用途のため、例えば車両周囲の画像を取得するための他のカメラを設けることもできる。車速センサ20は、自車両Vの走行速度を検出するものであって、例えば車輪に回転数を検知する車輪速センサで検出した車輪速から車速度を算出する。計算機30は、車両後方の立体物を検出するとともに、本例ではその立体物について移動距離及び移動速度を算出する。
As shown in FIG. 1, the camera 10 is attached to the host vehicle V so that the optical axis is at an angle θ from the horizontal to the lower side at a height h at the rear of the host vehicle V. The camera 10 images a predetermined area in the surrounding environment of the host vehicle V from this position. In the present embodiment, there is one camera 1 provided for detecting a three-dimensional object behind the host vehicle V. However, for other purposes, for example, providing another camera for acquiring an image around the vehicle. You can also. The vehicle speed sensor 20 detects the traveling speed of the host vehicle V, and calculates the vehicle speed from the wheel speed detected by, for example, a wheel speed sensor that detects the rotational speed of the wheel. The computer 30 detects a three-dimensional object behind the vehicle, and calculates a moving distance and a moving speed for the three-dimensional object in this example.
図2は、図1の自車両Vの走行状態を示す平面図である。同図に示すように、カメラ10は、所定の画角aで車両後方側を撮像する。このとき、カメラ10の画角aは、自車両Vが走行する車線に加えて、その左右の車線についても撮像可能な画角に設定されている。撮像可能な領域には、自車両Vの後方であり、自車両Vの走行車線の左右隣の隣接車線上の検出対象領域A1,A2を含む。
FIG. 2 is a plan view showing a traveling state of the host vehicle V in FIG. As shown in the figure, the camera 10 images the vehicle rear side at a predetermined angle of view a. At this time, the angle of view a of the camera 10 is set to an angle of view at which the left and right lanes can be imaged in addition to the lane in which the host vehicle V travels. The area that can be imaged includes detection target areas A1 and A2 on the adjacent lane that is behind the host vehicle V and that is adjacent to the left and right of the travel lane of the host vehicle V.
図3は、図1の計算機30の詳細を示すブロック図である。なお、図3においては、接続関係を明確とするためにカメラ10及び車速センサ20についても図示する。
FIG. 3 is a block diagram showing details of the computer 30 of FIG. In FIG. 3, the camera 10 and the vehicle speed sensor 20 are also shown in order to clarify the connection relationship.
図3に示すように、計算機30は、視点変換部31と、位置合わせ部32と、立体物検出部33と、検出領域設定部34と、スミア検出部40とを備える。本実施形態の計算部30は、差分波形情報を利用した立体物の検出ブロックに関する構成である。本実施形態の計算部30は、エッジ情報を利用した立体物の検出ブロックに関する構成とすることもできる。この場合は、図3に示す構成のうち、位置合わせ部32と、立体物検出部33から構成されるブロック構成Aを、破線で囲んだ輝度差算出部35と、エッジ線検出部36と、立体物検出部37から構成されるブロック構成Bと置き換えて構成することができる。もちろん、ブロック構成A及びブロック構成Bの両方を備え、差分波形情報を利用した立体物の検出を行うとともに、エッジ情報を利用した立体物の検出も行うことができるようにすることができる。ブロック構成A及びブロック構成Bを備える場合には、例えば明るさなどの環境要因に応じてブロック構成A又はブロック構成Bのいずれかを動作させることができる。以下、各構成について説明する。
As shown in FIG. 3, the computer 30 includes a viewpoint conversion unit 31, a positioning unit 32, a three-dimensional object detection unit 33, a detection area setting unit 34, and a smear detection unit 40. The calculation unit 30 of the present embodiment has a configuration relating to a three-dimensional object detection block using differential waveform information. The calculation unit 30 of the present embodiment can also be configured with respect to a three-dimensional object detection block using edge information. In this case, in the configuration shown in FIG. 3, a block configuration A configured by the alignment unit 32 and the three-dimensional object detection unit 33 is surrounded by a broken line, a luminance difference calculation unit 35, an edge line detection unit 36, It can be configured by replacing the block configuration B configured by the three-dimensional object detection unit 37. Of course, both the block configuration A and the block configuration B can be provided, so that the solid object can be detected using the difference waveform information and the solid object can be detected using the edge information. When the block configuration A and the block configuration B are provided, either the block configuration A or the block configuration B can be operated according to environmental factors such as brightness. Each configuration will be described below.
《差分波形情報による立体物の検出》
本実施形態の立体物検出装置1は、車両後方を撮像する単眼のカメラ1により得られた画像情報に基づいて車両後方の右側隣接車線の検出領域A1又は左側隣接車線の検出領域A2に存在する立体物を検出する。検出領域設定部34は、撮像された画像情報内であって、自車両Vの後方の右側及び左側のそれぞれに検出領域A1,A2を設定する。この検出領域A2,A2の位置は特に限定されず、また、処理条件に応じて適宜に設定することができる。特に、本実施形態の検出領域設定部34は、検出領域A1,A2のそれぞれについて、相対的に面積の大きい第1検出領域及び相対的に面積の小さい第2検出領域を有し、第1検出領域又は第2検出領域のいずれかを選択して設定することができる。後に詳述するが、本実施形態の検出領域設定部34は、立体物の存在を検出する際には相対的に面積の小さい第2検出領域を選択及び設定し、立体物の速度を検出する際には相対的に面積の大きい第1検出領域を選択及び設定する。つまり、立体物の存在を検出する処理において、立体物検出部33は、検出領域設定部34により設定された相対的に面積の小さい第2検出領域の画像情報に基づいて立体物の存否を判断し、立体物の移動距離、及び移動速度を検出する処理においては、検出領域設定部34により設定された相対的に面積の大きい第1検出領域の画像情報に基づいて移動速度を算出する。検出領域設定部34は、立体物検出部33(37)の要求に応じて、第1検出領域と第2検出領域を設定することができる。 <Detection of three-dimensional object by differential waveform information>
The three-dimensionalobject detection device 1 of the present embodiment exists in the detection area A1 of the right adjacent lane or the detection area A2 of the left adjacent lane behind the vehicle based on image information obtained by the monocular camera 1 that captures the rear of the vehicle. A three-dimensional object is detected. The detection area setting unit 34 sets detection areas A1 and A2 in the captured image information and on the right and left sides behind the host vehicle V, respectively. The positions of the detection areas A2 and A2 are not particularly limited, and can be set as appropriate according to the processing conditions. In particular, the detection region setting unit 34 of the present embodiment includes a first detection region having a relatively large area and a second detection region having a relatively small area for each of the detection regions A1 and A2, and the first detection region Either the region or the second detection region can be selected and set. As will be described in detail later, when detecting the presence of a three-dimensional object, the detection area setting unit 34 of the present embodiment selects and sets a second detection area having a relatively small area, and detects the speed of the three-dimensional object. In this case, the first detection region having a relatively large area is selected and set. That is, in the process of detecting the presence of a three-dimensional object, the three-dimensional object detection unit 33 determines the presence / absence of the three-dimensional object based on the image information of the second detection region having a relatively small area set by the detection region setting unit 34. In the process of detecting the moving distance and moving speed of the three-dimensional object, the moving speed is calculated based on the image information of the first detection area having a relatively large area set by the detection area setting unit 34. The detection area setting unit 34 can set the first detection area and the second detection area in response to a request from the three-dimensional object detection unit 33 (37).
本実施形態の立体物検出装置1は、車両後方を撮像する単眼のカメラ1により得られた画像情報に基づいて車両後方の右側隣接車線の検出領域A1又は左側隣接車線の検出領域A2に存在する立体物を検出する。検出領域設定部34は、撮像された画像情報内であって、自車両Vの後方の右側及び左側のそれぞれに検出領域A1,A2を設定する。この検出領域A2,A2の位置は特に限定されず、また、処理条件に応じて適宜に設定することができる。特に、本実施形態の検出領域設定部34は、検出領域A1,A2のそれぞれについて、相対的に面積の大きい第1検出領域及び相対的に面積の小さい第2検出領域を有し、第1検出領域又は第2検出領域のいずれかを選択して設定することができる。後に詳述するが、本実施形態の検出領域設定部34は、立体物の存在を検出する際には相対的に面積の小さい第2検出領域を選択及び設定し、立体物の速度を検出する際には相対的に面積の大きい第1検出領域を選択及び設定する。つまり、立体物の存在を検出する処理において、立体物検出部33は、検出領域設定部34により設定された相対的に面積の小さい第2検出領域の画像情報に基づいて立体物の存否を判断し、立体物の移動距離、及び移動速度を検出する処理においては、検出領域設定部34により設定された相対的に面積の大きい第1検出領域の画像情報に基づいて移動速度を算出する。検出領域設定部34は、立体物検出部33(37)の要求に応じて、第1検出領域と第2検出領域を設定することができる。 <Detection of three-dimensional object by differential waveform information>
The three-dimensional
また、本実施形態の検出領域設定部34は、検出領域A1,A2のそれぞれを設定する際に、車両後方の右側に設定された検出領域A1の第2検出領域を、車両後方の右側に設定された第1検出領域のうち車両前方側の右端に位置する頂点を含む領域を欠損させた領域とし、車両後方の左側に設定された検出領域A2の第2検出領域を、車両後方の左側に設定された第1検出領域のうち車両前方側の左端に位置する頂点を含む領域を欠損させた領域とする。後に詳述するが、本実施形態の検出領域設定部34は、立体物の存在を検出する際には右端又は左端に位置する頂点を含む領域を欠損させた第2検出領域を選択及び設定し、立体物の速度を検出する際には上記頂点を含む領域が欠損されていない第1検出領域を選択及び設定する。
In addition, when setting each of the detection areas A1 and A2, the detection area setting unit 34 of the present embodiment sets the second detection area of the detection area A1 set on the right side behind the vehicle on the right side behind the vehicle. In the first detection area, the area including the apex located at the right end on the front side of the vehicle is deleted, and the second detection area of the detection area A2 set on the left side on the rear side of the vehicle is set on the left side on the rear side of the vehicle. It is assumed that the region including the apex located at the left end on the front side of the vehicle in the set first detection region is a missing region. As will be described in detail later, when detecting the presence of a three-dimensional object, the detection region setting unit 34 of the present embodiment selects and sets a second detection region in which a region including a vertex located at the right end or the left end is missing. When detecting the speed of the three-dimensional object, the first detection region in which the region including the vertex is not lost is selected and set.
本実施形態の検出領域設定部34は、検出領域A1,A2のそれぞれを設定する際に、第1の検出領域と、この第1検出領域のうちカメラ10の設置位置から車両の側方方向の距離が所定距離以上の領域、つまり第1検出領域の外側(路肩側)の領域を欠損させた(車幅方向に狭めた)第2検出領域とを含む検出領域を設定する。
When setting each of the detection areas A1 and A2, the detection area setting unit 34 of the present embodiment sets the first detection area and the position of the camera 10 in the lateral direction of the vehicle from the first detection area. A detection area including a second detection area in which the distance is greater than or equal to a predetermined distance, that is, an area outside the first detection area (the road shoulder side) is missing (narrowed in the vehicle width direction) is set.
次に、視点変換部について説明する。視点変換部31は、カメラ10による撮像にて得られた所定領域の撮像画像データを入力し、入力した撮像画像データを鳥瞰視される状態の鳥瞰画像データに視点変換する。鳥瞰視される状態とは、上空から例えば鉛直下向きに見下ろす仮想カメラの視点から見た状態である。この視点変換は、例えば特開2008-219063号公報に記載されるようにして実行することができる。撮像画像データを鳥瞰視画像データに視点変換するのは、立体物に特有の鉛直エッジは鳥瞰視画像データへの視点変換により特定の定点を通る直線群に変換されるという原理に基づき、これを利用すれば平面物と立体物とを識別できるからである。なお、視点変換部31による画像変換処理の結果は、後述するエッジ情報による立体物の検出においても利用される。
Next, the viewpoint conversion unit will be described. The viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging with the camera 10 and converts the input captured image data into a bird's-eye image data in a bird's-eye view state. The state viewed from a bird's-eye view is a state viewed from the viewpoint of a virtual camera looking down from above, for example, vertically downward. This viewpoint conversion can be executed as described in, for example, Japanese Patent Application Laid-Open No. 2008-219063. The viewpoint conversion of captured image data to bird's-eye view image data is based on the principle that a vertical edge peculiar to a three-dimensional object is converted into a straight line group passing through a specific fixed point by viewpoint conversion to bird's-eye view image data. This is because a planar object and a three-dimensional object can be distinguished if used. Note that the result of the image conversion processing by the viewpoint conversion unit 31 is also used in detection of a three-dimensional object by edge information described later.
位置合わせ部32は、視点変換部31の視点変換により得られた鳥瞰画像データを順次入力し、入力した異なる時刻の鳥瞰画像データの位置を合わせる。図4は、位置合わせ部32の処理の概要を説明するための図であり、(a)は自車両Vの移動状態を示す平面図、(b)は位置合わせの概要を示す画像である。
The alignment unit 32 sequentially inputs the bird's-eye image data obtained by the viewpoint conversion of the viewpoint conversion unit 31, and aligns the positions of the inputted bird's-eye image data at different times. 4A and 4B are diagrams for explaining the outline of the processing of the alignment unit 32, where FIG. 4A is a plan view showing the moving state of the host vehicle V, and FIG. 4B is an image showing the outline of the alignment.
図4(a)に示すように、現時刻の自車両VがV1に位置し、一時刻前の自車両VがV2に位置していたとする。また、自車両Vの後側方向に他車両VXが位置して自車両Vと並走状態にあり、現時刻の他車両VXがV3に位置し、一時刻前の他車両VXがV4に位置していたとする。さらに、自車両Vは、一時刻で距離d移動したものとする。なお、一時刻前とは、現時刻から予め定められた時間(例えば1制御周期)だけ過去の時刻であってもよいし、任意の時間だけ過去の時刻であってもよい。
As shown in FIG. 4A, it is assumed that the host vehicle V at the current time is located at V1, and the host vehicle V one hour before is located at V2. Further, the other vehicle VX is located in the rear direction of the own vehicle V and is in parallel with the own vehicle V, the other vehicle VX at the current time is located at V3, and the other vehicle VX one hour before is located at V4. Suppose you were. Furthermore, it is assumed that the host vehicle V has moved a distance d at one time. Note that “one hour before” may be a past time for a predetermined time (for example, one control cycle) from the current time, or may be a past time for an arbitrary time.
このような状態において、現時刻における鳥瞰画像PBtは図4(b)に示すようになる。この鳥瞰画像PBtでは、路面上に描かれる白線については矩形状となり、比較的正確に平面視された状態となるが、位置V3にある他車両VXの位置については倒れ込みが発生する。また、一時刻前における鳥瞰画像PBt-1についても同様に、路面上に描かれる白線については矩形状となり、比較的正確に平面視された状態となるが、位置V4にある他車両VXについては倒れ込みが発生する。既述したとおり、立体物の鉛直エッジ(厳密な意味の鉛直エッジ以外にも路面から三次元空間に立ち上がったエッジを含む)は、鳥瞰視画像データへの視点変換処理によって倒れ込み方向に沿った直線群として現れるのに対し、路面上の平面画像は鉛直エッジを含まないので、視点変換してもそのような倒れ込みが生じないからである。
In this state, the bird's-eye image PB t at the current time is as shown in Figure 4 (b). In the bird's-eye image PB t, becomes a rectangular shape for the white line drawn on the road surface, but a relatively accurate is a plan view state, tilting occurs about the position of another vehicle VX at position V3. Similarly, for the bird's-eye view image PB t-1 one hour before, the white line drawn on the road surface has a rectangular shape and is relatively accurately viewed in plan, but the other vehicle VX at the position V4 Falls down. As described above, the vertical edges of solid objects (including the edges that rise in the three-dimensional space from the road surface in addition to the vertical edges in the strict sense) are straight lines along the collapse direction by the viewpoint conversion processing to bird's-eye view image data. This is because the plane image on the road surface does not include a vertical edge, but such a fall does not occur even when the viewpoint is changed.
位置合わせ部32は、上記のような鳥瞰画像PBt,PBt-1の位置合わせをデータ上で実行する。この際、位置合わせ部32は、一時刻前における鳥瞰画像PBt-1をオフセットさせ、現時刻における鳥瞰画像PBtと位置を一致させる。図4(b)の左側の画像と中央の画像は、移動距離d’だけオフセットした状態を示す。このオフセット量d’は、図4(a)に示した自車両Vの実際の移動距離dに対応する鳥瞰視画像データ上の移動量であり、車速センサ20からの信号と一時刻前から現時刻までの時間に基づいて決定される。
The alignment unit 32 performs alignment of the bird's-eye images PB t and PB t−1 as described above on the data. At this time, the alignment unit 32 is offset a bird's-eye view image PB t-1 before one unit time, to match the position and bird's-eye view image PB t at the current time. The image on the left side and the center image in FIG. 4B show a state that is offset by the movement distance d ′. This offset amount d ′ is a movement amount on the bird's-eye view image data corresponding to the actual movement distance d of the host vehicle V shown in FIG. It is determined based on the time until the time.
また、位置合わせ後において位置合わせ部32は、鳥瞰画像PBt,PBt-1の差分をとり、差分画像PDtのデータを生成する。ここで、差分画像PDtの画素値は、鳥瞰画像PBt,PBt-1の画素値の差を絶対値化したものでもよいし、照度環境の変化に対応するために当該絶対値が所定の閾値pを超えたときに「1」とし、超えないときに「0」としてもよい。図4(b)の右側の画像が、差分画像PDtである。
Further, after the alignment, the alignment unit 32 takes the difference between the bird's-eye images PB t and PB t−1 and generates data of the difference image PD t . Here, the pixel value of the difference image PD t may be an absolute value of the difference between the pixel values of the bird's-eye images PB t and PB t−1 , and the absolute value is predetermined in order to cope with a change in the illuminance environment. It may be set to “1” when the threshold value p is exceeded and “0” when the threshold value p is not exceeded. The image on the right side of FIG. 4B is the difference image PD t .
図3に戻り、立体物検出部33は、図4(b)に示す差分画像PDtのデータに基づいて立体物を検出する。この際、本例の立体物検出部33は、実空間上における立体物の移動距離についても算出する。立体物の検出及び移動距離の算出にあたり、立体物検出部33は、まず差分波形を生成する。なお、立体物の時間あたりの移動距離は、立体物の移動速度の算出に用いられる。そして、立体物の移動速度は、立体物が車両であるか否かの判断に用いることができる。
Returning to FIG. 3, the three-dimensional object detection unit 33 detects a three-dimensional object based on the data of the difference image PD t shown in FIG. At this time, the three-dimensional object detection unit 33 of this example also calculates the movement distance of the three-dimensional object in the real space. In detecting the three-dimensional object and calculating the movement distance, the three-dimensional object detection unit 33 first generates a differential waveform. Note that the moving distance of the three-dimensional object per time is used for calculating the moving speed of the three-dimensional object. The moving speed of the three-dimensional object can be used to determine whether or not the three-dimensional object is a vehicle.
差分波形の生成にあたって本実施形態の立体物検出部33は、差分画像PDtにおいて検出領域を設定する。本例の立体物検出装置1は、自車両Vの運転手が注意を払う他車両であり、特に、自車両Vが車線変更する際に接触の可能性がある自車両Vが走行する車線の隣の車線を走行する他車両を検出対象物として検出する。このため、画像情報に基づいて立体物を検出する本例では、カメラ1により得られた画像のうち、自車両Vの右側及び左側に二つの検出領域を設定する。具体的に、本実施形態では、図2に示すように自車両Vの後方の左側及び右側に矩形状の検出領域A1,A2を設定する。この検出領域A1,A2において検出された他車両は、自車両Vが走行する車線の隣の隣接車線を走行する障害物として検出される。なお、このような検出領域A1,A2は、自車両Vに対する相対位置から設定してもよいし、白線の位置を基準に設定してもよい。白線の位置を基準に設定する場合に、移動距離検出装置1は、例えば既存の白線認識技術等を利用するとよい。
Three-dimensional object detection unit 33 of the present embodiment when generating the differential waveform sets a detection area in the difference image PD t. The three-dimensional object detection device 1 of the present example is another vehicle that the driver of the host vehicle V pays attention to, in particular, the lane in which the host vehicle V that may be contacted when the host vehicle V changes lanes travels. Another vehicle traveling in the adjacent lane is detected as a detection target. For this reason, in this example which detects a solid object based on image information, two detection areas are set on the right side and the left side of the host vehicle V in the image obtained by the camera 1. Specifically, in the present embodiment, rectangular detection areas A1 and A2 are set on the left and right sides behind the host vehicle V as shown in FIG. The other vehicle detected in the detection areas A1 and A2 is detected as an obstacle traveling in the adjacent lane adjacent to the lane in which the host vehicle V is traveling. Such detection areas A1 and A2 may be set from a relative position with respect to the host vehicle V, or may be set based on the position of the white line. When setting the position of the white line as a reference, the movement distance detection device 1 may use, for example, an existing white line recognition technique.
また、立体物検出部33は、設定した検出領域A1,A2の自車両V側における辺(走行方向に沿う辺)を接地線L1,L2(図2)として認識する。一般に接地線は立体物が地面に接触する線を意味するが、本実施形態では地面に接触する線でなく上記の如くに設定される。なおこの場合であっても、経験上、本実施形態に係る接地線と、本来の他車両VXの位置から求められる接地線との差は大きくなり過ぎず、実用上は問題が無い。
Further, the three-dimensional object detection unit 33 recognizes the sides (sides along the traveling direction) of the set detection areas A1 and A2 on the own vehicle V side as the ground lines L1 and L2 (FIG. 2). In general, the ground line means a line in which the three-dimensional object contacts the ground. However, in the present embodiment, the ground line is set as described above, not a line in contact with the ground. Even in this case, from experience, the difference between the ground line according to the present embodiment and the ground line obtained from the position of the other vehicle VX is not too large, and there is no problem in practical use.
図5は、図3に示す立体物検出部33による差分波形の生成の様子を示す概略図である。図5に示すように、立体物検出部33は、位置合わせ部32で算出した差分画像PDt(図4(b)の右図)のうち検出領域A1,A2に相当する部分から、差分波形DWtを生成する。この際、立体物検出部33は、視点変換により立体物が倒れ込む方向に沿って、差分波形DWtを生成する。なお、図5に示す例では、便宜上検出領域A1のみを用いて説明するが、検出領域A2についても同様の手順で差分波形DWtを生成する。
FIG. 5 is a schematic diagram illustrating how a differential waveform is generated by the three-dimensional object detection unit 33 illustrated in FIG. 3. As shown in FIG. 5, the three-dimensional object detection unit 33 calculates a differential waveform from a portion corresponding to the detection areas A <b> 1 and A <b> 2 in the difference image PD t (right diagram in FIG. 4B) calculated by the alignment unit 32. DW t is generated. At this time, the three-dimensional object detection unit 33 generates a differential waveform DW t along the direction in which the three-dimensional object falls by viewpoint conversion. In the example shown in FIG. 5, only the detection area A1 is described for convenience, but the difference waveform DW t is generated for the detection area A2 in the same procedure.
具体的に説明すると、立体物検出部33は、差分画像DWtのデータ上において立体物が倒れ込む方向上の線Laを定義する。そして、立体物検出部33は、線La上において所定の差分を示す差分画素DPの数をカウントする。ここで、所定の差分を示す差分画素DPは、差分画像DWtの画素値が鳥瞰画像PBt,PBt-1の画素値の差を絶対値化したものである場合は、所定の閾値を超える画素であり、差分画像DWtの画素値が「0」「1」で表現されている場合は、「1」を示す画素である。
More specifically, the three-dimensional object detection unit 33 defines a line La in the direction in which the three-dimensional object falls on the data of the difference image DW t . Then, the three-dimensional object detection unit 33 counts the number of difference pixels DP indicating a predetermined difference on the line La. Here, the difference pixel DP indicating the predetermined difference has a predetermined threshold value when the pixel value of the difference image DW t is an absolute value of the difference between the pixel values of the bird's-eye images PB t and PB t−1. If the pixel value of the difference image DW t is expressed as “0” or “1”, the pixel indicates “1”.
立体物検出部33は、差分画素DPの数をカウントした後、線Laと接地線L1との交点CPを求める。そして、立体物検出部33は、交点CPとカウント数とを対応付け、交点CPの位置に基づいて横軸位置、すなわち図5右図の上下方向軸における位置を決定するとともに、カウント数から縦軸位置、すなわち図5右図の左右方向軸における位置を決定し、交点CPにおけるカウント数としてプロットする。
The three-dimensional object detection unit 33 counts the number of difference pixels DP and then obtains an intersection point CP between the line La and the ground line L1. Then, the three-dimensional object detection unit 33 associates the intersection CP with the count number, determines the horizontal axis position based on the position of the intersection CP, that is, the position on the vertical axis in the right diagram of FIG. The axis position, that is, the position on the right and left axis in the right diagram of FIG. 5 is determined and plotted as the count number at the intersection CP.
以下同様に、立体物検出部33は、立体物が倒れ込む方向上の線Lb,Lc…を定義して、差分画素DPの数をカウントし、各交点CPの位置に基づいて横軸位置を決定し、カウント数(差分画素DPの数)から縦軸位置を決定しプロットする。立体物検出部33は、上記を順次繰り返して度数分布化することで、図5右図に示すように差分波形DWtを生成する。
Similarly, the three-dimensional object detection unit 33 defines lines Lb, Lc... In the direction in which the three-dimensional object falls, counts the number of difference pixels DP, and determines the horizontal axis position based on the position of each intersection CP. Then, the vertical axis position is determined from the count number (number of difference pixels DP) and plotted. The three-dimensional object detection unit 33 generates the differential waveform DW t as shown in the right diagram of FIG.
なお、図5左図に示すように、立体物が倒れ込む方向上の線Laと線Lbとは検出領域A1と重複する距離が異なっている。このため、検出領域A1が差分画素DPで満たされているとすると、線Lb上よりも線La上の方が差分画素DPの数が多くなる。このため、立体物検出部33は、差分画素DPのカウント数から縦軸位置を決定する場合に、立体物が倒れ込む方向上の線La,Lbと検出領域A1とが重複する距離に基づいて正規化する。具体例を挙げると、図5左図において線La上の差分画素DPは6つあり、線Lb上の差分画素DPは5つである。このため、図5においてカウント数から縦軸位置を決定するにあたり、立体物検出部33は、カウント数を重複距離で除算するなどして正規化する。これにより、差分波形DWtに示すように、立体物が倒れ込む方向上の線La,Lbに対応する差分波形DWtの値はほぼ同じとなっている。
As shown in the left diagram of FIG. 5, the line La and the line Lb in the direction in which the three-dimensional object collapses have different distances overlapping the detection area A1. For this reason, if the detection area A1 is filled with the difference pixels DP, the number of difference pixels DP is larger on the line La than on the line Lb. For this reason, when the three-dimensional object detection unit 33 determines the vertical axis position from the count number of the difference pixels DP, the three-dimensional object detection unit 33 is normalized based on the distance at which the lines La and Lb in the direction in which the three-dimensional object falls and the detection area A1 overlap. Turn into. As a specific example, in the left diagram of FIG. 5, there are six difference pixels DP on the line La, and there are five difference pixels DP on the line Lb. For this reason, in determining the vertical axis position from the count number in FIG. 5, the three-dimensional object detection unit 33 normalizes the count number by dividing it by the overlap distance. Thus, as shown in the difference waveform DW t, the line La on the direction the three-dimensional object collapses, the value of the differential waveform DW t corresponding to Lb is substantially the same.
差分波形DWtの生成後、立体物検出部33は一時刻前の差分波形DWt-1との対比により移動距離を算出する。すなわち、立体物検出部33は、差分波形DWt,DWt-1の時間変化から移動距離を算出する。
After the generation of the differential waveform DW t , the three-dimensional object detection unit 33 calculates the movement distance by comparison with the differential waveform DW t−1 one time before. That is, the three-dimensional object detection unit 33 calculates the movement distance from the time change of the difference waveforms DW t and DW t−1 .
詳細に説明すると、立体物検出部33は、図6に示すように差分波形DWtを複数の小領域DWt1~DWtn(nは2以上の任意の整数)に分割する。図6は、立体物検出部33によって分割される小領域DWt1~DWtnを示す図である。小領域DWt1~DWtnは、例えば図6に示すように、互いに重複するようにして分割される。例えば小領域DWt1と小領域DWt2とは重複し、小領域DWt2と小領域DWt3とは重複する。
Specifically, as shown in FIG. 6, the three-dimensional object detection unit 33 divides the differential waveform DW t into a plurality of small areas DW t1 to DW tn (n is an arbitrary integer equal to or greater than 2). FIG. 6 is a diagram illustrating the small areas DW t1 to DW tn divided by the three-dimensional object detection unit 33. The small areas DW t1 to DW tn are divided so as to overlap each other, for example, as shown in FIG. For example, the small area DW t1 and the small area DW t2 overlap, and the small area DW t2 and the small area DW t3 overlap.
次いで、立体物検出部33は、小領域DWt1~DWtn毎にオフセット量(差分波形の横軸方向(図6の上下方向)の移動量)を求める。ここで、オフセット量は、一時刻前における差分波形DWt-1と現時刻における差分波形DWtとの差(横軸方向の距離)から求められる。この際、立体物検出部33は、小領域DWt1~DWtn毎に、一時刻前における差分波形DWt-1を横軸方向に移動させた際に、現時刻における差分波形DWtとの誤差が最小となる位置(横軸方向の位置)を判定し、差分波形DWt-1の元の位置と誤差が最小となる位置との横軸方向の移動量をオフセット量として求める。そして、立体物検出部33は、小領域DWt1~DWtn毎に求めたオフセット量をカウントしてヒストグラム化する。
Next, the three-dimensional object detection unit 33 obtains an offset amount (amount of movement of the differential waveform in the horizontal axis direction (vertical direction in FIG. 6)) for each of the small areas DW t1 to DW tn . Here, the offset amount is determined from the difference between the differential waveform DW t in the difference waveform DW t-1 and the current time before one unit time (distance in the horizontal axis direction). At this time, three-dimensional object detection unit 33, for each small area DW t1 ~ DW tn, when moving the differential waveform DW t1 before one unit time in the horizontal axis direction, the differential waveform DW t at the current time The position where the error is minimized (the position in the horizontal axis direction) is determined, and the amount of movement in the horizontal axis between the original position of the differential waveform DW t−1 and the position where the error is minimized is obtained as an offset amount. Then, the three-dimensional object detection unit 33 counts the offset amount obtained for each of the small areas DW t1 to DW tn and forms a histogram.
図7は、立体物検出部33により得られるヒストグラムの一例を示す図である。図7に示すように、各小領域DWt1~DWtnと一時刻前における差分波形DWt-1との誤差が最小となる移動量であるオフセット量には、多少のバラつきが生じる。このため、立体物検出部33は、バラつきを含んだオフセット量をヒストグラム化し、ヒストグラムから移動距離を算出する。この際、立体物検出部33は、ヒストグラムの極大値から立体物の移動距離を算出する。すなわち、図7に示す例において立体物検出部33は、ヒストグラムの極大値を示すオフセット量を移動距離τ*と算出する。なおこの移動距離τ*は、自車両Vに対する他車両VXの相対移動距離である。このため、立体物検出部33は、絶対移動距離を算出する場合には、得られた移動距離τ*と車速センサ20からの信号とに基づいて、絶対移動距離を算出することとなる。
FIG. 7 is a diagram illustrating an example of a histogram obtained by the three-dimensional object detection unit 33. As shown in FIG. 7, the offset amount, which is the amount of movement that minimizes the error between each of the small areas DW t1 to DW tn and the differential waveform DW t−1 one time before, has some variation. For this reason, the three-dimensional object detection unit 33 forms a histogram of offset amounts including variations, and calculates a movement distance from the histogram. At this time, the three-dimensional object detection unit 33 calculates the moving distance of the three-dimensional object from the maximum value of the histogram. That is, in the example illustrated in FIG. 7, the three-dimensional object detection unit 33 calculates the offset amount indicating the maximum value of the histogram as the movement distance τ * . The moving distance τ * is a relative moving distance of the other vehicle VX with respect to the host vehicle V. For this reason, when calculating the absolute movement distance, the three-dimensional object detection unit 33 calculates the absolute movement distance based on the obtained movement distance τ * and the signal from the vehicle speed sensor 20.
なお、ヒストグラム化にあたり立体物検出部33は、複数の小領域DWt1~DWtn毎に重み付けをし、小領域DWt1~DWtn毎に求めたオフセット量を重みに応じてカウントしてヒストグラム化してもよい。図8は、立体物検出部33による重み付けを示す図である。
Note that the three-dimensional object detection unit 33 weights each of the plurality of small areas DW t1 to DW tn and forms a histogram by counting the offset amount obtained for each of the small areas DW t1 to DW tn according to the weight. May be. FIG. 8 is a diagram illustrating weighting by the three-dimensional object detection unit 33.
図8に示すように、小領域DWm(mは1以上n-1以下の整数)は平坦となっている。すなわち、小領域DWmは所定の差分を示す画素数のカウントの最大値と最小値との差が小さくなっている。立体物検出部33は、このような小領域DWmについて重みを小さくする。平坦な小領域DWmについては、特徴がなくオフセット量の算出にあたり誤差が大きくなる可能性が高いからである。
As shown in FIG. 8, the small area DW m (m is an integer of 1 to n−1) is flat. That is, in the small area DW m , the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference is small. Three-dimensional object detection unit 33 to reduce the weight for such small area DW m. This is because the flat small area DW m has no characteristics and is likely to have a large error in calculating the offset amount.
一方、小領域DWm+k(kはn-m以下の整数)は起伏に富んでいる。すなわち、小領域DWmは所定の差分を示す画素数のカウントの最大値と最小値との差が大きくなっている。立体物検出部33は、このような小領域DWmについて重みを大きくする。起伏に富む小領域DWm+kについては、特徴的でありオフセット量の算出を正確に行える可能性が高いからである。このように重み付けすることにより、移動距離の算出精度を向上することができる。
On the other hand, the small region DW m + k (k is an integer equal to or less than nm) is rich in undulations. That is, in the small area DW m , the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference is large. Three-dimensional object detection unit 33 increases the weight for such small area DW m. This is because the small region DW m + k rich in undulations is characteristic and there is a high possibility that the offset amount can be accurately calculated. By weighting in this way, the calculation accuracy of the movement distance can be improved.
なお、移動距離の算出精度を向上するために上記実施形態では差分波形DWtを複数の小領域DWt1~DWtnに分割したが、移動距離の算出精度がさほど要求されない場合は小領域DWt1~DWtnに分割しなくてもよい。この場合に、立体物検出部33は、差分波形DWtと差分波形DWt-1との誤差が最小となるときの差分波形DWtのオフセット量から移動距離を算出することとなる。すなわち、一時刻前における差分波形DWt-1と現時刻における差分波形DWtとのオフセット量を求める方法は上記内容に限定されない。
In the above embodiment, the differential waveform DW t is divided into a plurality of small areas DW t1 to DW tn in order to improve the calculation accuracy of the movement distance. However, when the calculation accuracy of the movement distance is not so required, the small area DW t1 is divided. It is not necessary to divide into ~ DW tn . In this case, the three-dimensional object detection unit 33 calculates the moving distance from the offset amount of the differential waveform DW t when the error between the differential waveform DW t and the differential waveform DW t−1 is minimized. That is, the method for obtaining the offset amount of the difference waveform DW t in the difference waveform DW t-1 and the current time before one unit time is not limited to the above disclosure.
図3に戻り、計算機30はスミア検出部40を備える。スミア検出部40は、カメラ10による撮像によって得られた撮像画像のデータからスミアの発生領域を検出する。なお、スミアはCCDイメージセンサ等に生じる白飛び現象であることから、こうしたスミアが生じないCMOSイメージセンサ等を用いたカメラ10を採用する場合にはスミア検出部40を省略してもよい。
Returning to FIG. 3, the computer 30 includes a smear detection unit 40. The smear detection unit 40 detects a smear generation region from data of a captured image obtained by imaging with the camera 10. Since smear is a whiteout phenomenon that occurs in a CCD image sensor or the like, the smear detection unit 40 may be omitted when the camera 10 using a CMOS image sensor or the like that does not generate such smear is employed.
図9は、スミア検出部40による処理及びそれによる差分波形DWtの算出処理を説明するための画像図である。まずスミア検出部40にスミアSが存在する撮像画像Pのデータが入力されたとする。このとき、スミア検出部40は、撮像画像PからスミアSを検出する。スミアSの検出方法は様々であるが、例えば一般的なCCD(Charge-Coupled Device)カメラの場合、光源から画像下方向にだけスミアSが発生する。このため、本実施形態では画像下側から画像上方に向かって所定値以上の輝度値を持ち、且つ、縦方向に連続した領域を検索し、これをスミアSの発生領域と特定する。
FIG. 9 is an image diagram for explaining the processing by the smear detection unit 40 and the calculation processing of the differential waveform DW t thereby. First, it is assumed that data of the captured image P in which the smear S exists is input to the smear detection unit 40. At this time, the smear detection unit 40 detects the smear S from the captured image P. There are various methods for detecting the smear S. For example, in the case of a general CCD (Charge-Coupled Device) camera, the smear S is generated only in the downward direction of the image from the light source. For this reason, in this embodiment, a region having a luminance value equal to or higher than a predetermined value from the lower side of the image to the upper side of the image and continuous in the vertical direction is searched, and this is identified as a smear S generation region.
また、スミア検出部40は、スミアSの発生箇所について画素値を「1」とし、それ以外の箇所を「0」とするスミア画像SPのデータを生成する。生成後、スミア検出部40はスミア画像SPのデータを視点変換部31に送信する。また、スミア画像SPのデータを入力した視点変換部31は、このデータを鳥瞰視される状態に視点変換する。これにより、視点変換部31はスミア鳥瞰画像SBtのデータを生成する。生成後、視点変換部31はスミア鳥瞰画像SBtのデータを位置合わせ部33に送信する。また、視点変換部31は一時刻前のスミア鳥瞰画像SBt-1のデータを位置合わせ部33に送信する。
In addition, the smear detection unit 40 generates smear image SP data in which the pixel value is set to “1” for the place where the smear S occurs and the other place is set to “0”. After the generation, the smear detection unit 40 transmits the data of the smear image SP to the viewpoint conversion unit 31. In addition, the viewpoint conversion unit 31 to which the data of the smear image SP is input converts the viewpoint into a state of bird's-eye view. As a result, the viewpoint conversion unit 31 generates data of the smear bird's-eye view image SB t . After the generation, the viewpoint conversion unit 31 transmits the data of the smear bird's-eye view image SB t to the alignment unit 33. In addition, the viewpoint conversion unit 31 transmits the data of the smear bird's-eye view image SB t−1 one hour before to the alignment unit 33.
位置合わせ部32は、スミア鳥瞰画像SBt,SBt-1の位置合わせをデータ上で実行する。具体的な位置合わせについては、鳥瞰画像PBt,PBt-1の位置合わせをデータ上で実行する場合と同様である。また、位置合わせ後、位置合わせ部32は、各スミア鳥瞰画像SBt,SBt-1のスミアSの発生領域について論理和をとる。これにより、位置合わせ部32は、マスク画像MPのデータを生成する。生成後、位置合わせ部32は、マスク画像MPのデータを立体物検出部33に送信する。
The alignment unit 32 aligns the smear bird's-eye images SB t and SB t−1 on the data. The specific alignment is the same as the case where the alignment of the bird's-eye images PB t and PB t−1 is executed on the data. In addition, after the alignment, the alignment unit 32 performs a logical sum on the smear S generation region of each smear bird's-eye view image SB t , SB t−1 . Thereby, the alignment part 32 produces | generates the data of mask image MP. After the generation, the alignment unit 32 transmits the data of the mask image MP to the three-dimensional object detection unit 33.
立体物検出部33は、マスク画像MPのうちスミアSの発生領域に該当する箇所について、度数分布のカウント数をゼロとする。すなわち、図9に示すような差分波形DWtが生成されていた場合に、立体物検出部33は、スミアSによるカウント数SCをゼロとし、補正された差分波形DWt’を生成することとなる。
The three-dimensional object detection unit 33 sets the count number of the frequency distribution to zero for the portion corresponding to the smear S generation region in the mask image MP. That is, when the differential waveform DW t as shown in FIG. 9 is generated, the three-dimensional object detection unit 33 sets the count number SC by the smear S to zero and generates a corrected differential waveform DW t ′. Become.
なお、本実施形態において立体物検出部33は、車両V(カメラ10)の移動速度を求め、求めた移動速度から静止物についてのオフセット量を求める。静止物のオフセット量を求めた後、立体物検出部33は、ヒストグラムの極大値のうち静止物に該当するオフセット量を無視したうえで、立体物の移動距離を算出する。
In the present embodiment, the three-dimensional object detection unit 33 obtains the moving speed of the vehicle V (camera 10), and obtains the offset amount for the stationary object from the obtained moving speed. After obtaining the offset amount of the stationary object, the three-dimensional object detection unit 33 calculates the moving distance of the three-dimensional object after ignoring the offset amount corresponding to the stationary object among the maximum values of the histogram.
図10は、立体物検出部33により得られるヒストグラムの他例を示す図である。カメラ10の画角内に他車両VXの他に静止物が存在する場合に、得られるヒストグラムには2つの極大値τ1,τ2が現れる。この場合、2つの極大値τ1,τ2のうち、いずれか一方は静止物のオフセット量である。このため、立体物検出部33は、移動速度から静止物についてのオフセット量を求め、そのオフセット量に該当する極大値について無視し、残り一方の極大値を採用して立体物の移動距離を算出する。
FIG. 10 is a diagram illustrating another example of a histogram obtained by the three-dimensional object detection unit 33. When a stationary object exists in addition to the other vehicle VX within the angle of view of the camera 10, two maximum values τ1 and τ2 appear in the obtained histogram. In this case, one of the two maximum values τ1, τ2 is the offset amount of the stationary object. For this reason, the three-dimensional object detection unit 33 calculates the offset amount for the stationary object from the moving speed, ignores the maximum value corresponding to the offset amount, and calculates the moving distance of the three-dimensional object by using the remaining maximum value. To do.
なお、静止物に該当するオフセット量を無視したとしても、極大値が複数存在する場合、カメラ10の画角内に他車両VXが複数台存在すると想定される。しかし、検出領域A1,A2内に複数の他車両VXが存在することは極めて稀である。このため、立体物検出部33は、移動距離の算出を中止する。
Even if the offset amount corresponding to the stationary object is ignored, if there are a plurality of maximum values, it is assumed that there are a plurality of other vehicles VX within the angle of view of the camera 10. However, it is very rare that a plurality of other vehicles VX exist in the detection areas A1 and A2. For this reason, the three-dimensional object detection unit 33 stops calculating the movement distance.
次に差分波形情報による立体物検出手順を説明する。図11及び図12は、本実施形態の立体物検出手順を示すフローチャートである。図11に示すように、先ずステップS0において、計算機30は所定のルールに基づいて検出領域を設定する。この検出領域の設定手法については後に詳述する。そして、計算機30は、カメラ10による撮像画像Pのデータを入力し、スミア検出部40によりスミア画像SPを生成する(S1)。次いで、視点変換部31は、カメラ10からの撮像画像Pのデータから鳥瞰画像PBtのデータを生成すると共に、スミア画像SPのデータからスミア鳥瞰画像SBtのデータを生成する(S2)。
Next, a solid object detection procedure based on differential waveform information will be described. 11 and 12 are flowcharts showing the three-dimensional object detection procedure of this embodiment. As shown in FIG. 11, first in step S0, the computer 30 sets a detection area based on a predetermined rule. This detection area setting method will be described in detail later. Then, the computer 30 receives data of the image P captured by the camera 10 and generates a smear image SP by the smear detection unit 40 (S1). Next, the viewpoint conversion unit 31 generates data of the bird's-eye view image PB t from the data of the captured image P from the camera 10, and also generates data of the smear bird's-eye view image SB t from the data of the smear image SP (S2).
そして、位置合わせ部33は、鳥瞰画像PBtのデータと、一時刻前の鳥瞰画像PBt-1のデータとを位置合わせすると共に、スミア鳥瞰画像SBtのデータと、一時刻前のスミア鳥瞰画像SBt-1のデータとを位置合わせする(S3)。この位置合わせ後、位置合わせ部33は、差分画像PDtのデータを生成すると共に、マスク画像MPのデータを生成する(S4)。その後、立体物検出部33は、差分画像PDtのデータと、一時刻前の差分画像PDt-1のデータとから、差分波形DWtを生成する(S5)。差分波形DWtを生成後、立体物検出部33は、差分波形DWtのうち、スミアSの発生領域に該当するカウント数をゼロとし、スミアSによる影響を抑制する(S6)。
Then, the alignment unit 33 aligns the data of the bird's-eye view image PB t and the data of the bird's-eye view image PB t-1 of the previous time, and the data of the smear bird's-eye view image SB t and the smear bird's-eye view of the previous time. The data of the image SB t-1 is aligned (S3). After this alignment, the alignment unit 33 generates data for the difference image PD t and also generates data for the mask image MP (S4). Then, three-dimensional object detection unit 33, the data of the difference image PD t, and a one unit time before the difference image PD t-1 of the data, generates a difference waveform DW t (S5). After generating the differential waveform DW t , the three-dimensional object detection unit 33 sets the count number corresponding to the smear S generation region in the differential waveform DW t to zero, and suppresses the influence of the smear S (S6).
その後、立体物検出部33は、差分波形DWtのピークが第1閾値α以上であるか否かを判断する(S7)。ここで、差分波形DWtのピークが第1閾値α以上でない場合、すなわち差分が殆どない場合には、撮像画像P内には立体物が存在しないと考えられる。このため、差分波形DWtのピークが第1閾値α以上でないと判断した場合には(S7:NO)、立体物検出部33は、立体物が存在せず、障害物としての他車両が存在しないと判断する(図12:S16)。そして、図11及び図12に示す処理を終了する。
Thereafter, the three-dimensional object detection unit 33 determines whether or not the peak of the differential waveform DW t is greater than or equal to the first threshold value α (S7). Here, when the peak of the difference waveform DW t is not equal to or greater than the first threshold value α, that is, when there is almost no difference, it is considered that there is no three-dimensional object in the captured image P. For this reason, when it is determined that the peak of the differential waveform DW t is not equal to or greater than the first threshold value α (S7: NO), the three-dimensional object detection unit 33 does not have a three-dimensional object and has another vehicle as an obstacle. It is determined not to do so (FIG. 12: S16). Then, the processes shown in FIGS. 11 and 12 are terminated.
一方、差分波形DWtのピークが第1閾値α以上であると判断した場合には(S7:YES)、立体物検出部33は、立体物が存在すると判断し、差分波形DWtを複数の小領域DWt1~DWtnに分割する(S8)。次いで、立体物検出部33は、小領域DWt1~DWtn毎に重み付けを行う(S9)。その後、立体物検出部33は、小領域DWt1~DWtn毎のオフセット量を算出し(S10)、重みを加味してヒストグラムを生成する(S11)。
On the other hand, when it is determined that the peak of the difference waveform DW t is equal to or greater than the first threshold value α (S7: YES), the three-dimensional object detection unit 33 determines that a three-dimensional object exists, and sets the difference waveform DW t to a plurality of difference waveforms DW t . The area is divided into small areas DW t1 to DW tn (S8). Next, the three-dimensional object detection unit 33 performs weighting for each of the small areas DW t1 to DW tn (S9). Thereafter, the three-dimensional object detection unit 33 calculates an offset amount for each of the small areas DW t1 to DW tn (S10), and generates a histogram with weights added (S11).
そして、立体物検出部33は、ヒストグラムに基づいて自車両Vに対する立体物の移動距離である相対移動距離を算出する(S12)。次に、立体物検出部33は、相対移動距離から立体物の絶対移動速度を算出する(S13)。このとき、立体物検出部33は、相対移動距離を時間微分して相対移動速度を算出すると共に、車速センサ20で検出された自車速を加算して、絶対移動速度を算出する。
Then, the three-dimensional object detection unit 33 calculates a relative movement distance that is a movement distance of the three-dimensional object with respect to the host vehicle V based on the histogram (S12). Next, the three-dimensional object detection unit 33 calculates the absolute movement speed of the three-dimensional object from the relative movement distance (S13). At this time, the three-dimensional object detection unit 33 calculates the relative movement speed by differentiating the relative movement distance with respect to time, and adds the own vehicle speed detected by the vehicle speed sensor 20 to calculate the absolute movement speed.
その後、立体物検出部33は、立体物の絶対移動速度が10km/h以上、且つ、立体物の自車両Vに対する相対移動速度が+60km/h以下であるか否かを判断する(S14)。双方を満たす場合には(S14:YES)、立体物検出部33は、立体物が他車両VXであると判断する(S15)。そして、図11及び図12に示す処理を終了する。一方、いずれか一方でも満たさない場合には(S14:NO)、立体物検出部33は、他車両が存在しないと判断する(S16)。そして、図11及び図12に示す処理を終了する。
Thereafter, the three-dimensional object detection unit 33 determines whether the absolute movement speed of the three-dimensional object is 10 km / h or more and the relative movement speed of the three-dimensional object with respect to the host vehicle V is +60 km / h or less (S14). When both are satisfied (S14: YES), the three-dimensional object detection unit 33 determines that the three-dimensional object is the other vehicle VX (S15). Then, the processes shown in FIGS. 11 and 12 are terminated. On the other hand, when either one is not satisfied (S14: NO), the three-dimensional object detection unit 33 determines that there is no other vehicle (S16). Then, the processes shown in FIGS. 11 and 12 are terminated.
本実施形態では、立体物が検出領域内に存在するか否かを判断する処理の後に立体物の移動速度(移動距離)を算出する処理を行う例を示すが、この順序は特に限定されない。つまり、立体物の移動速度が所定値域内である場合に、差分波形情報に基づいて立体物の検出処理を行ってもよい。また、立体物の検出(存否)処理と立体物の移動速度の算出処理を同時に並行して行ってもよい。
In the present embodiment, an example of performing a process of calculating a moving speed (movement distance) of a three-dimensional object after a process of determining whether or not a three-dimensional object exists in the detection region is shown, but this order is not particularly limited. That is, when the moving speed of the three-dimensional object is within the predetermined value range, the three-dimensional object detection process may be performed based on the difference waveform information. In addition, the three-dimensional object detection (presence / absence) process and the three-dimensional object movement speed calculation process may be performed in parallel.
なお、本実施形態では自車両Vの後側方を検出領域A1,A2とし、自車両Vが走行中に注意を払うべきである自車両の走行車線の隣を走行する隣接車線を走行する他車両VXを検出すること、特に、自車両Vが車線変更した場合に接触する可能性がある否かに重点を置いている。自車両Vが車線変更した場合に、自車両の走行車線の隣の隣接車線を走行する他車両VXと接触する可能性がある否かを判断するためである。このため、ステップS14の処理が実行されている。すなわち、本実施形態にけるシステムを高速道路で作動させることを前提とすると、立体物の速度が10km/h未満である場合、たとえ他車両VXが存在したとしても、車線変更する際には自車両Vの遠く後方に位置するため問題となることが少ない。同様に、立体物の自車両Vに対する相対移動速度が+60km/hを超える場合(すなわち、立体物が自車両Vの速度よりも60km/hより大きな速度で移動している場合)、車線変更する際には自車両Vの前方に移動しているため問題となることが少ない。このため、ステップS14では車線変更の際に問題となる他車両VXを判断しているともいえる。
In the present embodiment, the rear side of the host vehicle V is set as the detection areas A1 and A2, and the vehicle V travels in the adjacent lane that travels next to the travel lane of the host vehicle to which attention should be paid while traveling. Emphasis is placed on detecting the vehicle VX, and in particular, whether or not there is a possibility of contact when the host vehicle V changes lanes. This is to determine whether or not there is a possibility of contact with another vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the own vehicle when the own vehicle V changes lanes. For this reason, the process of step S14 is performed. That is, assuming that the system according to this embodiment is operated on a highway, if the speed of a three-dimensional object is less than 10 km / h, even if another vehicle VX exists, Since it is located far behind the vehicle V, there are few problems. Similarly, when the relative moving speed of the three-dimensional object with respect to the own vehicle V exceeds +60 km / h (that is, when the three-dimensional object is moving at a speed higher than 60 km / h than the speed of the own vehicle V), the lane is changed. In some cases, since the vehicle is moving in front of the host vehicle V, there is little problem. For this reason, it can be said that the other vehicle VX which becomes a problem at the time of lane change is judged in step S14.
また、ステップS14において立体物の絶対移動速度が10km/h以上、且つ、立体物の自車両Vに対する相対移動速度が+60km/h以下であるかを判断することにより、以下の効果がある。例えば、カメラ10の取り付け誤差によっては、静止物の絶対移動速度を数km/hであると検出してしまう場合があり得る。よって、10km/h以上であるかを判断することにより、静止物を他車両VXであると判断してしまう可能性を低減することができる。また、ノイズによっては立体物の自車両Vに対する相対速度を+60km/hを超える速度に検出してしまうことがあり得る。よって、相対速度が+60km/h以下であるかを判断することにより、ノイズによる誤検出の可能性を低減できる。
In step S14, it is determined whether the absolute moving speed of the three-dimensional object is 10 km / h or more and the relative moving speed of the three-dimensional object with respect to the vehicle V is +60 km / h or less. For example, depending on the mounting error of the camera 10, the absolute moving speed of the stationary object may be detected to be several km / h. Therefore, by determining whether the speed is 10 km / h or more, it is possible to reduce the possibility of determining that the stationary object is the other vehicle VX. Further, depending on the noise, the relative speed of the three-dimensional object with respect to the host vehicle V may be detected at a speed exceeding +60 km / h. Therefore, the possibility of erroneous detection due to noise can be reduced by determining whether the relative speed is +60 km / h or less.
さらに、ステップS14の処理に代えて、絶対移動速度がマイナスでないことや、0km/hでないことを判断してもよい。また、本実施形態では自車両Vが車線変更した場合に接触する可能性がある否かに重点を置いているため、ステップS15において他車両VXが検出された場合に、自車両の運転者に警告音を発したり、所定の表示装置により警告相当の表示を行ったりしてもよい。
Furthermore, instead of the process of step S14, it may be determined that the absolute movement speed is not negative or not 0 km / h. Further, in the present embodiment, since emphasis is placed on whether or not there is a possibility of contact when the host vehicle V changes lanes, when another vehicle VX is detected in step S15, the driver of the host vehicle is notified. A warning sound may be emitted or a display corresponding to a warning may be performed by a predetermined display device.
このように、本例の差分波形情報による立体物の検出手順によれば、視点変換により立体物が倒れ込む方向に沿って、差分画像PDtのデータ上において所定の差分を示す画素数をカウントして度数分布化することで差分波形DWtを生成する。ここで、差分画像PDtのデータ上において所定の差分を示す画素とは、異なる時刻の画像において変化があった画素であり、言い換えれば立体物が存在した箇所であるといえる。このため、立体物が存在した箇所において、立体物が倒れ込む方向に沿って画素数をカウントして度数分布化することで差分波形DWtを生成することとなる。特に、立体物が倒れ込む方向に沿って画素数をカウントすることから、立体物に対して高さ方向の情報から差分波形DWtを生成することとなる。そして、高さ方向の情報を含む差分波形DWtの時間変化から立体物の移動距離を算出する。このため、単に1点の移動のみに着目するような場合と比較して、時間変化前の検出箇所と時間変化後の検出箇所とは高さ方向の情報を含んで特定されるため立体物において同じ箇所となり易く、同じ箇所の時間変化から移動距離を算出することとなり、移動距離の算出精度を向上させることができる。
Thus, according to the detection procedure of the three-dimensional object based on the difference waveform information of this example, the number of pixels indicating a predetermined difference is counted on the data of the difference image PD t along the direction in which the three-dimensional object falls by viewpoint conversion. The difference waveform DW t is generated by frequency distribution. Here, the pixel indicating the predetermined difference on the data of the difference image PD t is a pixel that has changed in an image at a different time, in other words, a place where a three-dimensional object exists. For this reason, the difference waveform DW t is generated by counting the number of pixels along the direction in which the three-dimensional object collapses and performing frequency distribution at the location where the three-dimensional object exists. In particular, since the number of pixels is counted along the direction in which the three-dimensional object falls, the differential waveform DW t is generated from the information in the height direction for the three-dimensional object. Then, the moving distance of the three-dimensional object is calculated from the time change of the differential waveform DW t including the information in the height direction. For this reason, compared with the case where only one point of movement is focused on, the detection location before the time change and the detection location after the time change are specified including information in the height direction. The same location is likely to be obtained, and the movement distance is calculated from the time change of the same location, so that the calculation accuracy of the movement distance can be improved.
また、差分波形DWtのうちスミアSの発生領域に該当する箇所について、度数分布のカウント数をゼロとする。これにより、差分波形DWtのうちスミアSによって生じる波形部位を除去することとなり、スミアSを立体物と誤認してしまう事態を防止することができる。
In addition, the count number of the frequency distribution is set to zero for the portion corresponding to the smear S generation region in the differential waveform DW t . As a result, the waveform portion generated by the smear S in the differential waveform DW t is removed, and a situation in which the smear S is mistaken as a three-dimensional object can be prevented.
また、異なる時刻に生成された差分波形DWtの誤差が最小となるときの差分波形DWtのオフセット量から立体物の移動距離を算出する。このため、波形という1次元の情報のオフセット量から移動距離を算出することとなり、移動距離の算出にあたり計算コストを抑制することができる。
Further, the moving distance of the three-dimensional object is calculated from the offset amount of the differential waveform DW t when the error of the differential waveform DW t generated at different times is minimized. For this reason, the movement distance is calculated from the offset amount of the one-dimensional information called the waveform, and the calculation cost can be suppressed in calculating the movement distance.
また、異なる時刻に生成された差分波形DWtを複数の小領域DWt1~DWtnに分割する。このように複数の小領域DWt1~DWtnに分割することによって、立体物のそれぞれの箇所を表わした波形を複数得ることとなる。また、小領域DWt1~DWtn毎にそれぞれの波形の誤差が最小となるときのオフセット量を求め、小領域DWt1~DWtn毎に求めたオフセット量をカウントしてヒストグラム化することにより、立体物の移動距離を算出する。このため、立体物のそれぞれの箇所毎にオフセット量を求めることとなり、複数のオフセット量から移動距離を求めることとなり、移動距離の算出精度を向上させることができる。
Further, the differential waveform DW t generated at different times is divided into a plurality of small regions DW t1 to DW tn . Thus, by dividing into a plurality of small areas DW t1 to DW tn , a plurality of waveforms representing respective portions of the three-dimensional object are obtained. Also, determine the offset amount when the error of each waveform for each small area DW t1 ~ DW tn is minimized by histogram by counting the offset amount determined for each small area DW t1 ~ DW tn, The moving distance of the three-dimensional object is calculated. For this reason, the offset amount is obtained for each part of the three-dimensional object, and the movement distance is obtained from a plurality of offset amounts, so that the calculation accuracy of the movement distance can be improved.
また、複数の小領域DWt1~DWtn毎に重み付けをし、小領域DWt1~DWtn毎に求めたオフセット量を重みに応じてカウントしてヒストグラム化する。このため、特徴的な領域については重みを大きくし、特徴的でない領域については重みを小さくすることにより、一層適切に移動距離を算出することができる。従って、移動距離の算出精度を一層向上させることができる。
Further, weighting is performed for each of the plurality of small areas DW t1 to DW tn , and the offset amount obtained for each of the small areas DW t1 to DW tn is counted according to the weight to form a histogram. For this reason, the moving distance can be calculated more appropriately by increasing the weight for the characteristic area and decreasing the weight for the non-characteristic area. Therefore, the calculation accuracy of the moving distance can be further improved.
また、差分波形DWtの各小領域DWt1~DWtnについて、所定の差分を示す画素数のカウントの最大値と最小値との差が大きいほど、重みを大きくする。このため、最大値と最小値との差が大きい特徴的な起伏の領域ほど重みが大きくなり、起伏が小さい平坦な領域については重みが小さくなる。ここで、平坦な領域よりも起伏の大きい領域の方が形状的にオフセット量を正確に求めやすいため、最大値と最小値との差が大きい領域ほど重みを大きくすることにより、移動距離の算出精度を一層向上させることができる。
For each of the small areas DW t1 to DW tn of the differential waveform DW t , the weight is increased as the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference increases. For this reason, the characteristic undulation region having a large difference between the maximum value and the minimum value has a larger weight, and the flat region having a small undulation has a smaller weight. Here, since it is easier to obtain the offset amount more accurately in the shape of the undulating area than in the flat area, the moving distance is calculated by increasing the weight in the area where the difference between the maximum value and the minimum value is large. The accuracy can be further improved.
また、小領域DWt1~DWtn毎に求めたオフセット量をカウントして得られたヒストグラムの極大値から、立体物の移動距離を算出する。このため、オフセット量にバラつきがあったとしても、その極大値から、より正確性の高い移動距離を算出することができる。
Further, the moving distance of the three-dimensional object is calculated from the maximum value of the histogram obtained by counting the offset amount obtained for each of the small areas DW t1 to DW tn . For this reason, even if there is a variation in the offset amount, a more accurate movement distance can be calculated from the maximum value.
また、静止物についてのオフセット量を求め、このオフセット量を無視するため、静止物により立体物の移動距離の算出精度が低下してしまう事態を防止することができる。また、静止物に該当するオフセット量を無視したうえで、極大値が複数ある場合、立体物の移動距離の算出を中止する。このため、極大値が複数あるような誤った移動距離を算出してしまう事態を防止することができる。
Also, since the offset amount for a stationary object is obtained and this offset amount is ignored, it is possible to prevent a situation in which the calculation accuracy of the moving distance of the three-dimensional object is lowered due to the stationary object. In addition, if there are a plurality of maximum values after ignoring the offset amount corresponding to the stationary object, the calculation of the moving distance of the three-dimensional object is stopped. For this reason, it is possible to prevent a situation in which an erroneous movement distance having a plurality of maximum values is calculated.
なお上記実施形態において、自車両Vの車速を車速センサ20からの信号に基づいて判断しているが、これに限らず、異なる時刻の複数の画像から速度を推定するようにしてもよい。この場合、車速センサが不要となり、構成の簡素化を図ることができる。
In the above embodiment, the vehicle speed of the host vehicle V is determined based on a signal from the vehicle speed sensor 20, but the present invention is not limited to this, and the speed may be estimated from a plurality of images at different times. In this case, a vehicle speed sensor becomes unnecessary, and the configuration can be simplified.
また、上記実施形態においては撮像した現時刻の画像と一時刻前の画像とを鳥瞰図に変換し、変換した鳥瞰図の位置合わせを行ったうえで差分画像PDtを生成し、生成した差分画像PDtを倒れ込み方向(撮像した画像を鳥瞰図に変換した際の立体物の倒れ込み方向)に沿って評価して差分波形DWtを生成しているが、これに限定されない。例えば、一時刻前の画像のみを鳥瞰図に変換し、変換した鳥瞰図を位置合わせした後に再び撮像した画像相当に変換し、この画像と現時刻の画像とで差分画像を生成し、生成した差分画像を倒れ込み方向に相当する方向(すなわち、倒れ込み方向を撮像画像上の方向に変換した方向)に沿って評価することによって差分波形DWtを生成してもよい。すなわち、現時刻の画像と一時刻前の画像との位置合わせを行い、位置合わせを行った両画像の差分から差分画像PDtを生成し、差分画像PDtを鳥瞰図に変換した際の立体物の倒れ込み方向に沿って評価できれば、必ずしも明確に鳥瞰図を生成しなくともよい。
In the above-described embodiment, the captured image at the current time and the image one hour before are converted into a bird's-eye view, the converted bird's-eye view is aligned, the difference image PD t is generated, and the generated difference image PD Although t is evaluated along the falling direction (the falling direction of the three-dimensional object when the captured image is converted into a bird's eye view), the differential waveform DW t is generated, but the present invention is not limited to this. For example, only the image one hour before is converted into a bird's-eye view, the converted bird's-eye view is converted into an equivalent to the image captured again, a difference image is generated between this image and the current time image, and the generated difference image The differential waveform DW t may be generated by evaluating along the direction corresponding to the falling direction (that is, the direction in which the falling direction is converted into the direction on the captured image). That is, the three-dimensional object when the image of the current time and the image of one hour before are aligned, the difference image PD t is generated from the difference between the two images subjected to the alignment, and the difference image PD t is converted into a bird's eye view The bird's-eye view does not necessarily have to be clearly generated as long as the evaluation can be performed along the direction in which the user falls.
《エッジ情報による立体物の検出》
次に、図3に示すブロックAに代えて動作させることが可能である、輝度差算出部35、エッジ線検出部36及び立体物検出部37で構成されるエッジ情報を利用した立体物の検出ブロックBについて説明する。図13は、図3のカメラ10の撮像範囲等を示す図であり、図13(a)は平面図、図13(b)は、自車両Vから後側方における実空間上の斜視図を示す。図13(a)に示すように、カメラ10は所定の画角aとされ、この所定の画角aに含まれる自車両Vから後側方を撮像する。カメラ10の画角aは、図2に示す場合と同様に、カメラ10の撮像範囲に自車両Vが走行する車線に加えて、隣接する車線も含まれるように設定されている。 《Detection of solid objects by edge information》
Next, it is possible to operate instead of the block A shown in FIG. 3 to detect a three-dimensional object using edge information configured by a luminancedifference calculation unit 35, an edge line detection unit 36, and a three-dimensional object detection unit 37. The block B will be described. FIGS. 13A and 13B are diagrams illustrating an imaging range and the like of the camera 10 in FIG. 3. FIG. 13A is a plan view, and FIG. 13B is a perspective view in real space on the rear side from the host vehicle V. Show. As shown in FIG. 13A, the camera 10 has a predetermined angle of view a, and images the rear side from the host vehicle V included in the predetermined angle of view a. Similarly to the case shown in FIG. 2, the angle of view “a” of the camera 10 is set so that the imaging range of the camera 10 includes the adjacent lane in addition to the lane in which the host vehicle V travels.
次に、図3に示すブロックAに代えて動作させることが可能である、輝度差算出部35、エッジ線検出部36及び立体物検出部37で構成されるエッジ情報を利用した立体物の検出ブロックBについて説明する。図13は、図3のカメラ10の撮像範囲等を示す図であり、図13(a)は平面図、図13(b)は、自車両Vから後側方における実空間上の斜視図を示す。図13(a)に示すように、カメラ10は所定の画角aとされ、この所定の画角aに含まれる自車両Vから後側方を撮像する。カメラ10の画角aは、図2に示す場合と同様に、カメラ10の撮像範囲に自車両Vが走行する車線に加えて、隣接する車線も含まれるように設定されている。 《Detection of solid objects by edge information》
Next, it is possible to operate instead of the block A shown in FIG. 3 to detect a three-dimensional object using edge information configured by a luminance
本例の検出領域A1,A2は、平面視(鳥瞰視された状態)において台形状とされ、これら検出領域A1,A2の位置、大きさ及び形状は、距離d1~d4に基づいて決定される。なお、同図に示す例の検出領域A1,A2は台形状に限らず、図2に示すように鳥瞰視された状態で矩形など他の形状であってもよい。なお、本実施形態における検出領域設定部34も、先述した手法により検出領域A1,A2を設定することができる。
The detection areas A1 and A2 in this example are trapezoidal in a plan view (when viewed from a bird's eye), and the positions, sizes, and shapes of the detection areas A1 and A2 are determined based on the distances d 1 to d 4. Is done. The detection areas A1 and A2 in the example shown in the figure are not limited to a trapezoidal shape, and may be other shapes such as a rectangle when viewed from a bird's eye view as shown in FIG. Note that the detection area setting unit 34 in the present embodiment can also set the detection areas A1 and A2 by the method described above.
ここで、距離d1は、自車両Vから接地線L1,L2までの距離である。接地線L1,L2は、自車両Vが走行する車線に隣接する車線に存在する立体物が地面に接触する線を意味する。本実施形態においては、自車両Vの後側方において自車両Vの車線に隣接する左右の車線を走行する他車両VX等(2輪車等を含む)を検出することが目的である。このため、自車両Vから白線Wまでの距離d11及び白線Wから他車両VXが走行すると予測される位置までの距離d12から、他車両VXの接地線L1,L2となる位置である距離d1を略固定的に決定しておくことができる。
Here, the distance d1 is a distance from the host vehicle V to the ground lines L1 and L2. The ground lines L1 and L2 mean lines on which a three-dimensional object existing in the lane adjacent to the lane in which the host vehicle V travels contacts the ground. The purpose of the present embodiment is to detect other vehicles VX and the like (including two-wheeled vehicles) traveling in the left and right lanes adjacent to the lane of the host vehicle V on the rear side of the host vehicle V. For this reason, a distance d1 which is a position to be the ground lines L1 and L2 of the other vehicle VX is obtained from a distance d11 from the own vehicle V to the white line W and a distance d12 from the white line W to a position where the other vehicle VX is predicted to travel. It can be determined substantially fixedly.
また、距離d1については、固定的に決定されている場合に限らず、可変としてもよい。この場合に、計算機30は、白線認識等の技術により自車両Vに対する白線Wの位置を認識し、認識した白線Wの位置に基づいて距離d11を決定する。これにより、距離d1は、決定された距離d11を用いて可変的に設定される。以下の本実施形態においては、他車両VXが走行する位置(白線Wからの距離d12)及び自車両Vが走行する位置(白線Wからの距離d11)は大凡決まっていることから、距離d1は固定的に決定されているものとする。
Further, the distance d1 is not limited to being fixedly determined, and may be variable. In this case, the computer 30 recognizes the position of the white line W with respect to the host vehicle V by a technique such as white line recognition, and determines the distance d11 based on the recognized position of the white line W. Thereby, the distance d1 is variably set using the determined distance d11. In the following embodiment, since the position where the other vehicle VX travels (distance d12 from the white line W) and the position where the host vehicle V travels (distance d11 from the white line W) are roughly determined, the distance d1 is It shall be fixedly determined.
距離d2は、自車両Vの後端部から車両進行方向に伸びる距離である。この距離d2は、検出領域A1,A2が少なくともカメラ10の画角a内に収まるように決定されている。特に本実施形態において、距離d2は、画角aに区分される範囲に接するよう設定されている。距離d3は、検出領域A1,A2の車両進行方向における長さを示す距離である。この距離d3は、検出対象となる立体物の大きさに基づいて決定される。本実施形態においては、検出対象が他車両VX等であるため、距離d3は、他車両VXを含む長さに設定される。
The distance d2 is a distance extending from the rear end portion of the host vehicle V in the vehicle traveling direction. The distance d2 is determined so that the detection areas A1 and A2 are at least within the angle of view a of the camera 10. In particular, in the present embodiment, the distance d2 is set so as to be in contact with the range divided into the angle of view a. The distance d3 is a distance indicating the length of the detection areas A1, A2 in the vehicle traveling direction. This distance d3 is determined based on the size of the three-dimensional object to be detected. In the present embodiment, since the detection target is the other vehicle VX or the like, the distance d3 is set to a length including the other vehicle VX.
距離d4は、図13(b)に示すように、実空間において他車両VX等のタイヤを含むように設定された高さを示す距離である。距離d4は、鳥瞰視画像においては図13(a)に示す長さとされる。なお、距離d4は、鳥瞰視画像において左右の隣接車線よりも更に隣接する車線(すなわち2車線隣りの車線)を含まない長さとすることもできる。自車両Vの車線から2車線隣の車線を含んでしまうと、自車両Vが走行している車線である自車線の左右の隣接車線に他車両VXが存在するのか、2車線隣りの車線に他車両VXが存在するのかについて、区別が付かなくなってしまうためである。
As shown in FIG. 13B, the distance d4 is a distance indicating a height set so as to include a tire such as the other vehicle VX in the real space. The distance d4 is a length shown in FIG. 13A in the bird's-eye view image. The distance d4 may be a length that does not include a lane that is further adjacent to the left and right adjacent lanes in the bird's-eye view image (that is, a lane that is adjacent to two lanes). If the lane adjacent to the two lanes is included from the lane of the own vehicle V, there is another vehicle VX in the adjacent lane on the left and right of the own lane that is the lane in which the own vehicle V is traveling, or in the lane adjacent to the two lanes This is because it becomes impossible to distinguish whether there is another vehicle VX.
以上のように、距離d1~距離d4が決定され、これにより検出領域A1,A2の位置、大きさ及び形状が決定される。具体的に説明すると、距離d1により、台形をなす検出領域A1,A2の上辺b1の位置が決定される。距離d2により、上辺b1の始点位置C1が決定される。距離d3により、上辺b1の終点位置C2が決定される。カメラ10から始点位置C1に向かって伸びる直線L3により、台形をなす検出領域A1,A2の側辺b2が決定される。同様に、カメラ10から終点位置C2に向かって伸びる直線L4により、台形をなす検出領域A1,A2の側辺b3が決定される。距離d4により、台形をなす検出領域A1,A2の下辺b4の位置が決定される。このように、各辺b1~b4により囲まれる領域が検出領域A1,A2とされる。この検出領域A1,A2は、図13(b)に示すように、自車両Vから後側方における実空間上では真四角(長方形)となる。
As described above, the distances d1 to d4 are determined, and thereby the positions, sizes, and shapes of the detection areas A1 and A2 are determined. More specifically, the position of the upper side b1 of the detection areas A1 and A2 forming a trapezoid is determined by the distance d1. The starting point position C1 of the upper side b1 is determined by the distance d2. The end point position C2 of the upper side b1 is determined by the distance d3. The side b2 of the detection areas A1 and A2 having a trapezoidal shape is determined by a straight line L3 extending from the camera 10 toward the starting point position C1. Similarly, a side b3 of trapezoidal detection areas A1 and A2 is determined by a straight line L4 extending from the camera 10 toward the end position C2. The position of the lower side b4 of the detection areas A1 and A2 having a trapezoidal shape is determined by the distance d4. Thus, the areas surrounded by the sides b1 to b4 are set as the detection areas A1 and A2. As shown in FIG. 13B, the detection areas A <b> 1 and A <b> 2 are true squares (rectangles) in the real space behind the host vehicle V.
図3に戻り、視点変換部31は、カメラ10による撮像にて得られた所定領域の撮像画像データを入力する。視点変換部31は、入力した撮像画像データに対して、鳥瞰視される状態の鳥瞰画像データに視点変換処理を行う。鳥瞰視される状態とは、上空から例えば鉛直下向き(又は、やや斜め下向き)に見下ろす仮想カメラの視点から見た状態である。この視点変換処理は、例えば特開2008-219063号公報に記載された技術によって実現することができる。
Returning to FIG. 3, the viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging with the camera 10. The viewpoint conversion unit 31 performs viewpoint conversion processing on the input captured image data to the bird's-eye image data in a bird's-eye view state. The bird's-eye view is a state seen from the viewpoint of a virtual camera looking down from above, for example, vertically downward (or slightly obliquely downward). This viewpoint conversion process can be realized by a technique described in, for example, Japanese Patent Application Laid-Open No. 2008-219063.
輝度差算出部35は、鳥瞰視画像に含まれる立体物のエッジを検出するために、視点変換部31により視点変換された鳥瞰視画像データに対して、輝度差の算出を行う。輝度差算出部35は、実空間における鉛直方向に伸びる鉛直仮想線に沿った複数の位置ごとに、当該各位置の近傍の2つの画素間の輝度差を算出する。輝度差算出部35は、実空間における鉛直方向に伸びる鉛直仮想線を1本だけ設定する手法と、鉛直仮想線を2本設定する手法との何れかによって輝度差を算出することができる。
The luminance difference calculation unit 35 calculates a luminance difference with respect to the bird's-eye view image data subjected to viewpoint conversion by the viewpoint conversion unit 31 in order to detect the edge of the three-dimensional object included in the bird's-eye view image. For each of a plurality of positions along a vertical imaginary line extending in the vertical direction in the real space, the brightness difference calculating unit 35 calculates a brightness difference between two pixels in the vicinity of each position. The luminance difference calculation unit 35 can calculate the luminance difference by either a method of setting only one vertical virtual line extending in the vertical direction in the real space or a method of setting two vertical virtual lines.
鉛直仮想線を2本設定する具体的な手法について説明する。輝度差算出部35は、視点変換された鳥瞰視画像に対して、実空間で鉛直方向に伸びる線分に該当する第1鉛直仮想線と、第1鉛直仮想線と異なり実空間で鉛直方向に伸びる線分に該当する第2鉛直仮想線とを設定する。輝度差算出部35は、第1鉛直仮想線上の点と第2鉛直仮想線上の点との輝度差を、第1鉛直仮想線及び第2鉛直仮想線に沿って連続的に求める。以下、この輝度差算出部35の動作について詳細に説明する。
A specific method for setting two vertical virtual lines will be described. The brightness difference calculation unit 35 applies a first vertical imaginary line corresponding to a line segment extending in the vertical direction in the real space and a vertical direction in the real space different from the first vertical imaginary line with respect to the bird's-eye view image that has undergone viewpoint conversion. A second vertical imaginary line corresponding to the extending line segment is set. The luminance difference calculation unit 35 continuously obtains a luminance difference between a point on the first vertical imaginary line and a point on the second vertical imaginary line along the first vertical imaginary line and the second vertical imaginary line. Hereinafter, the operation of the luminance difference calculation unit 35 will be described in detail.
輝度差算出部35は、図14(a)に示すように、実空間で鉛直方向に伸びる線分に該当し、且つ、検出領域A1を通過する第1鉛直仮想線La(以下、注目線Laという)を設定する。また輝度差算出部35は、注目線Laと異なり、実空間で鉛直方向に伸びる線分に該当し、且つ、検出領域A1を通過する第2鉛直仮想線Lr(以下、参照線Lrという)を設定する。ここで参照線Lrは、実空間における所定距離だけ注目線Laから離間する位置に設定される。なお、実空間で鉛直方向に伸びる線分に該当する線とは、鳥瞰視画像においてはカメラ10の位置Psから放射状に広がる線となる。この放射状に広がる線は、鳥瞰視に変換した際に立体物が倒れ込む方向に沿う線である。
As shown in FIG. 14A, the luminance difference calculation unit 35 corresponds to a line segment extending in the vertical direction in the real space and passes through the detection area A1 (hereinafter referred to as the attention line La). Set). In addition, unlike the attention line La, the luminance difference calculation unit 35 corresponds to a line segment extending in the vertical direction in the real space and also passes through the second vertical virtual line Lr (hereinafter referred to as a reference line Lr) passing through the detection area A1. Set. Here, the reference line Lr is set at a position separated from the attention line La by a predetermined distance in the real space. Note that the line corresponding to the line segment extending in the vertical direction in the real space is a line that spreads radially from the position Ps of the camera 10 in the bird's-eye view image. This radially extending line is a line along the direction in which the three-dimensional object falls when converted to bird's-eye view.
輝度差算出部35は、注目線La上に注目点Pa(第1鉛直仮想線上の点)を設定する。また輝度差算出部35は、参照線Lr上に参照点Pr(第2鉛直板想線上の点)を設定する。これら注目線La、注目点Pa、参照線Lr、参照点Prは、実空間上において図14(b)に示す関係となる。図14(b)から明らかなように、注目線La及び参照線Lrは、実空間上において鉛直方向に伸びた線であり、注目点Paと参照点Prとは、実空間上において略同じ高さに設定される点である。なお、注目点Paと参照点Prとは必ずしも厳密に同じ高さである必要はなく、注目点Paと参照点Prとが同じ高さとみなせる程度の誤差は許容される。
The luminance difference calculation unit 35 sets the attention point Pa (point on the first vertical imaginary line) on the attention line La. In addition, the luminance difference calculation unit 35 sets a reference point Pr (a point on the second vertical plate) on the reference line Lr. The attention line La, the attention point Pa, the reference line Lr, and the reference point Pr have the relationship shown in FIG. 14B in the real space. As is clear from FIG. 14B, the attention line La and the reference line Lr are lines extending in the vertical direction in the real space, and the attention point Pa and the reference point Pr are substantially the same height in the real space. This is the point that is set. Note that the attention point Pa and the reference point Pr do not necessarily have the same height, and an error that allows the attention point Pa and the reference point Pr to be regarded as the same height is allowed.
輝度差算出部35は、注目点Paと参照点Prとの輝度差を求める。仮に、注目点Paと参照点Prとの輝度差が大きいと、注目点Paと参照点Prとの間にエッジが存在すると考えられる。このため、図3に示したエッジ線検出部36は、注目点Paと参照点Prとの輝度差に基づいてエッジ線を検出する。
The luminance difference calculation unit 35 obtains a luminance difference between the attention point Pa and the reference point Pr. If the luminance difference between the attention point Pa and the reference point Pr is large, it is considered that an edge exists between the attention point Pa and the reference point Pr. Therefore, the edge line detection unit 36 shown in FIG. 3 detects an edge line based on the luminance difference between the attention point Pa and the reference point Pr.
この点をより詳細に説明する。図15は、輝度差算出部35の詳細動作を示す図であり、図15(a)は鳥瞰視された状態の鳥瞰視画像を示し、図15(b)は、図15(a)に示した鳥瞰視画像の一部B1を拡大した図である。なお図15についても検出領域A1のみを図示して説明するが、検出領域A2についても同様の手順で輝度差を算出する。
This point will be explained in more detail. FIG. 15 is a diagram illustrating a detailed operation of the luminance difference calculation unit 35, in which FIG. 15 (a) shows a bird's-eye view image in a bird's-eye view state, and FIG. 15 (b) is shown in FIG. 15 (a). It is the figure which expanded a part B1 of the bird's-eye view image. Although only the detection area A1 is illustrated and described in FIG. 15, the luminance difference is calculated in the same procedure for the detection area A2.
カメラ10が撮像した撮像画像内に他車両VXが映っていた場合に、図15(a)に示すように、鳥瞰視画像内の検出領域A1に他車両VXが現れる。図15(b)に図15(a)中の領域B1の拡大図を示すように、鳥瞰視画像上において、他車両VXのタイヤのゴム部分上に注目線Laが設定されていたとする。この状態において、輝度差算出部35は、先ず参照線Lrを設定する。参照線Lrは、注目線Laから実空間上において所定の距離だけ離れた位置に、鉛直方向に沿って設定される。具体的には、本実施形態に係る立体物検出装置1において、参照線Lrは、注目線Laから実空間上において10cmだけ離れた位置に設定される。これにより、参照線Lrは、鳥瞰視画像上において、例えば他車両VXのタイヤのゴムから10cm相当だけ離れた他車両VXのタイヤのホイール上に設定される。
When the other vehicle VX is reflected in the captured image captured by the camera 10, the other vehicle VX appears in the detection area A1 in the bird's-eye view image as shown in FIG. As shown in the enlarged view of the area B1 in FIG. 15A in FIG. 15B, it is assumed that the attention line La is set on the rubber part of the tire of the other vehicle VX on the bird's-eye view image. In this state, the luminance difference calculation unit 35 first sets the reference line Lr. The reference line Lr is set along the vertical direction at a position away from the attention line La by a predetermined distance in the real space. Specifically, in the three-dimensional object detection device 1 according to the present embodiment, the reference line Lr is set at a position separated from the attention line La by 10 cm in real space. Thereby, the reference line Lr is set on the wheel of the tire of the other vehicle VX that is separated from the rubber of the tire of the other vehicle VX by, for example, 10 cm on the bird's eye view image.
次に、輝度差算出部35は、注目線La上に複数の注目点Pa1~PaNを設定する。図15(b)においては、説明の便宜上、6つの注目点Pa1~Pa6(以下、任意の点を示す場合には単に注目点Paiという)を設定している。なお、注目線La上に設定する注目点Paの数は任意でよい。以下の説明では、N個の注目点Paが注目線La上に設定されたものとして説明する。
Next, the luminance difference calculation unit 35 sets a plurality of attention points Pa1 to PaN on the attention line La. In FIG. 15B, for the convenience of explanation, six attention points Pa1 to Pa6 (hereinafter simply referred to as attention point Pai when an arbitrary point is indicated) are set. Note that the number of attention points Pa set on the attention line La may be arbitrary. In the following description, it is assumed that N attention points Pa are set on the attention line La.
次に、輝度差算出部35は、実空間上において各注目点Pa1~PaNと同じ高さとなるように各参照点Pr1~PrNを設定する。そして、輝度差算出部35は、同じ高さ同士の注目点Paと参照点Prとの輝度差を算出する。これにより、輝度差算出部35は、実空間における鉛直方向に伸びる鉛直仮想線に沿った複数の位置(1~N)ごとに、2つの画素の輝度差を算出する。輝度差算出部35は、例えば第1注目点Pa1とは、第1参照点Pr1との間で輝度差を算出し、第2注目点Pa2とは、第2参照点Pr2との間で輝度差を算出することとなる。これにより、輝度差算出部35は、注目線La及び参照線Lrに沿って、連続的に輝度差を求める。すなわち、輝度差算出部35は、第3~第N注目点Pa3~PaNと第3~第N参照点Pr3~PrNとの輝度差を順次求めていくこととなる。
Next, the luminance difference calculation unit 35 sets the reference points Pr1 to PrN so as to be the same height as the attention points Pa1 to PaN in the real space. Then, the luminance difference calculation unit 35 calculates the luminance difference between the attention point Pa and the reference point Pr having the same height. Thereby, the luminance difference calculation unit 35 calculates the luminance difference between the two pixels for each of a plurality of positions (1 to N) along the vertical imaginary line extending in the vertical direction in the real space. For example, the luminance difference calculating unit 35 calculates a luminance difference between the first attention point Pa1 and the first reference point Pr1, and the second difference between the second attention point Pa2 and the second reference point Pr2. Will be calculated. Thereby, the luminance difference calculation unit 35 continuously calculates the luminance difference along the attention line La and the reference line Lr. That is, the luminance difference calculation unit 35 sequentially obtains the luminance difference between the third to Nth attention points Pa3 to PaN and the third to Nth reference points Pr3 to PrN.
輝度差算出部35は、検出領域A1内において注目線Laをずらしながら、上記の参照線Lrの設定、注目点Pa及び参照点Prの設定、輝度差の算出といった処理を繰り返し実行する。すなわち、輝度差算出部35は、注目線La及び参照線Lrのそれぞれを、実空間上において接地線L1の延在方向に同一距離だけ位置を変えながら上記の処理を繰り返し実行する。輝度差算出部35は、例えば、前回処理において参照線Lrとなっていた線を注目線Laに設定し、この注目線Laに対して参照線Lrを設定して、順次輝度差を求めていくことになる。
The luminance difference calculation unit 35 repeatedly executes the above-described processing such as setting the reference line Lr, setting the attention point Pa and the reference point Pr, and calculating the luminance difference while shifting the attention line La in the detection area A1. That is, the luminance difference calculation unit 35 repeatedly executes the above processing while changing the positions of the attention line La and the reference line Lr by the same distance in the extending direction of the ground line L1 in the real space. For example, the luminance difference calculation unit 35 sets the reference line Lr as the reference line Lr in the previous processing, sets the reference line Lr for the attention line La, and sequentially obtains the luminance difference. It will be.
図3に戻り、エッジ線検出部36は、輝度差算出部35により算出された連続的な輝度差から、エッジ線を検出する。例えば、図15(b)に示す場合、第1注目点Pa1と第1参照点Pr1とは、同じタイヤ部分に位置するために、輝度差は、小さい。一方、第2~第6注目点Pa2~Pa6はタイヤのゴム部分に位置し、第2~第6参照点Pr2~Pr6はタイヤのホイール部分に位置する。したがって、第2~第6注目点Pa2~Pa6と第2~第6参照点Pr2~Pr6との輝度差は大きくなる。このため、エッジ線検出部36は、輝度差が大きい第2~第6注目点Pa2~Pa6と第2~第6参照点Pr2~Pr6との間にエッジ線が存在することを検出することができる。
3, the edge line detection unit 36 detects an edge line from the continuous luminance difference calculated by the luminance difference calculation unit 35. For example, in the case illustrated in FIG. 15B, the first attention point Pa <b> 1 and the first reference point Pr <b> 1 are located in the same tire portion, and thus the luminance difference is small. On the other hand, the second to sixth attention points Pa2 to Pa6 are located in the rubber part of the tire, and the second to sixth reference points Pr2 to Pr6 are located in the wheel part of the tire. Therefore, the luminance difference between the second to sixth attention points Pa2 to Pa6 and the second to sixth reference points Pr2 to Pr6 becomes large. Therefore, the edge line detection unit 36 may detect that an edge line exists between the second to sixth attention points Pa2 to Pa6 and the second to sixth reference points Pr2 to Pr6 having a large luminance difference. it can.
具体的には、エッジ線検出部36は、エッジ線を検出するにあたり、先ず下記の数式1に従って、i番目の注目点Pai(座標(xi,yi))とi番目の参照点Pri(座標(xi’,yi’))との輝度差から、i番目の注目点Paiに属性付けを行う。
[数1]
I(xi,yi)>I(xi’,yi’)+tのとき
s(xi,yi)=1
I(xi,yi)<I(xi’,yi’)-tのとき
s(xi,yi)=-1
上記以外のとき
s(xi,yi)=0 Specifically, when detecting the edge line, the edgeline detection unit 36 firstly follows the following Equation 1 to determine the i-th attention point Pai (coordinate (xi, yi)) and the i-th reference point Pri (coordinate ( xi ′, yi ′)) and the i th attention point Pai are attributed.
[Equation 1]
When I (xi, yi)> I (xi ′, yi ′) + t s (xi, yi) = 1
When I (xi, yi) <I (xi ′, yi ′) − t s (xi, yi) = − 1
Otherwise s (xi, yi) = 0
[数1]
I(xi,yi)>I(xi’,yi’)+tのとき
s(xi,yi)=1
I(xi,yi)<I(xi’,yi’)-tのとき
s(xi,yi)=-1
上記以外のとき
s(xi,yi)=0 Specifically, when detecting the edge line, the edge
[Equation 1]
When I (xi, yi)> I (xi ′, yi ′) + t s (xi, yi) = 1
When I (xi, yi) <I (xi ′, yi ′) − t s (xi, yi) = − 1
Otherwise s (xi, yi) = 0
上記数式1において、tは閾値を示し、I(xi,yi)はi番目の注目点Paiの輝度値を示し、I(xi’,yi’)はi番目の参照点Priの輝度値を示す。上記数式1によれば、注目点Paiの輝度値が、参照点Priに閾値tを加えた輝度値よりも高い場合には、当該注目点Paiの属性s(xi,yi)は‘1’となる。一方、注目点Paiの輝度値が、参照点Priから閾値tを減じた輝度値よりも低い場合には、当該注目点Paiの属性s(xi,yi)は‘-1’となる。注目点Paiの輝度値と参照点Priの輝度値とがそれ以外の関係である場合には、注目点Paiの属性s(xi,yi)は‘0’となる。
In Equation 1, t represents a threshold value, I (xi, yi) represents the luminance value of the i-th attention point Pai, and I (xi ′, yi ′) represents the luminance value of the i-th reference point Pri. . According to Equation 1, when the luminance value of the attention point Pai is higher than the luminance value obtained by adding the threshold value t to the reference point Pri, the attribute s (xi, yi) of the attention point Pai is “1”. Become. On the other hand, when the luminance value of the attention point Pai is lower than the luminance value obtained by subtracting the threshold value t from the reference point Pri, the attribute s (xi, yi) of the attention point Pai is “−1”. When the luminance value of the attention point Pai and the luminance value of the reference point Pri are in other relationships, the attribute s (xi, yi) of the attention point Pai is “0”.
次にエッジ線検出部36は、下記数式2に基づいて、注目線Laに沿った属性sの連続性c(xi,yi)から、注目線Laがエッジ線であるか否かを判定する。
[数2]
s(xi,yi)=s(xi+1,yi+1)のとき(且つ0=0を除く)、
c(xi,yi)=1
上記以外のとき、
c(xi,yi)=0 Next, the edgeline detection unit 36 determines whether or not the attention line La is an edge line from the continuity c (xi, yi) of the attribute s along the attention line La based on Equation 2 below.
[Equation 2]
When s (xi, yi) = s (xi + 1, yi + 1) (and excluding 0 = 0),
c (xi, yi) = 1
Other than the above
c (xi, yi) = 0
[数2]
s(xi,yi)=s(xi+1,yi+1)のとき(且つ0=0を除く)、
c(xi,yi)=1
上記以外のとき、
c(xi,yi)=0 Next, the edge
[Equation 2]
When s (xi, yi) = s (xi + 1, yi + 1) (and excluding 0 = 0),
c (xi, yi) = 1
Other than the above
c (xi, yi) = 0
注目点Paiの属性s(xi,yi)と隣接する注目点Pai+1の属性s(xi+1,yi+1)とが同じである場合には、連続性c(xi,yi)は‘1’となる。注目点Paiの属性s(xi,yi)と隣接する注目点Pai+1の属性s(xi+1,yi+1)とが同じではない場合には、連続性c(xi,yi)は‘0’となる。
When the attribute s (xi, yi) of the attention point Pai and the attribute s (xi + 1, yi + 1) of the adjacent attention point Pai + 1 are the same, the continuity c (xi, yi) is “1”. When the attribute s (xi, yi) of the attention point Pai is not the same as the attribute s (xi + 1, yi + 1) of the adjacent attention point Pai + 1, the continuity c (xi, yi) is “0”.
次にエッジ線検出部36は、注目線La上の全ての注目点Paの連続性cについて総和を求める。エッジ線検出部36は、求めた連続性cの総和を注目点Paの数Nで割ることにより、連続性cを正規化する。エッジ線検出部36は、正規化した値が閾値θを超えた場合に、注目線Laをエッジ線と判断する。なお、閾値θは、予め実験等によって設定された値である。
Next, the edge line detection unit 36 obtains the sum for the continuity c of all the points of interest Pa on the line of interest La. The edge line detection unit 36 normalizes the continuity c by dividing the obtained sum of continuity c by the number N of points of interest Pa. The edge line detection unit 36 determines that the attention line La is an edge line when the normalized value exceeds the threshold θ. The threshold value θ is a value set in advance through experiments or the like.
すなわち、エッジ線検出部36は、下記数式3に基づいて注目線Laがエッジ線であるか否かを判断する。そして、エッジ線検出部36は、検出領域A1上に描かれた注目線Laの全てについてエッジ線であるか否かを判断する。
[数3]
Σc(xi,yi)/N>θ That is, the edgeline detection unit 36 determines whether or not the attention line La is an edge line based on Equation 3 below. Then, the edge line detection unit 36 determines whether or not all the attention lines La drawn on the detection area A1 are edge lines.
[Equation 3]
Σc (xi, yi) / N> θ
[数3]
Σc(xi,yi)/N>θ That is, the edge
[Equation 3]
Σc (xi, yi) / N> θ
図3に戻り、立体物検出部37は、エッジ線検出部36により検出されたエッジ線の量に基づいて立体物を検出する。上述したように、本実施形態に係る立体物検出装置1は、実空間上において鉛直方向に伸びるエッジ線を検出する。鉛直方向に伸びるエッジ線が多く検出されるということは、検出領域A1,A2に立体物が存在する可能性が高いということである。このため、立体物検出部37は、エッジ線検出部36により検出されたエッジ線の量に基づいて立体物を検出する。さらに、立体物検出部37は、立体物を検出するに先立って、エッジ線検出部36により検出されたエッジ線が正しいものであるか否かを判定する。立体物検出部37は、エッジ線上の鳥瞰視画像のエッジ線に沿った輝度変化が所定の閾値よりも大きいか否かを判定する。エッジ線上の鳥瞰視画像の輝度変化が閾値よりも大きい場合には、当該エッジ線が誤判定により検出されたものと判断する。一方、エッジ線上の鳥瞰視画像の輝度変化が閾値よりも大きくない場合には、当該エッジ線が正しいものと判定する。なお、この閾値は、実験等により予め設定された値である。
3, the three-dimensional object detection unit 37 detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 36. As described above, the three-dimensional object detection device 1 according to the present embodiment detects an edge line extending in the vertical direction in real space. The fact that many edge lines extending in the vertical direction are detected means that there is a high possibility that a three-dimensional object exists in the detection areas A1 and A2. For this reason, the three-dimensional object detection unit 37 detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 36. Furthermore, prior to detecting the three-dimensional object, the three-dimensional object detection unit 37 determines whether or not the edge line detected by the edge line detection unit 36 is correct. The three-dimensional object detection unit 37 determines whether or not the luminance change along the edge line of the bird's-eye view image on the edge line is larger than a predetermined threshold value. When the luminance change of the bird's-eye view image on the edge line is larger than the threshold value, it is determined that the edge line is detected by erroneous determination. On the other hand, when the luminance change of the bird's-eye view image on the edge line is not larger than the threshold value, it is determined that the edge line is correct. This threshold value is a value set in advance by experiments or the like.
図16は、エッジ線の輝度分布を示す図であり、図16(a)は検出領域A1に立体物としての他車両VXが存在した場合のエッジ線及び輝度分布を示し、図16(b)は検出領域A1に立体物が存在しない場合のエッジ線及び輝度分布を示す。
FIG. 16 is a diagram illustrating the luminance distribution of the edge line. FIG. 16A illustrates the edge line and the luminance distribution when another vehicle VX as a three-dimensional object exists in the detection area A1, and FIG. Indicates an edge line and a luminance distribution when there is no solid object in the detection area A1.
図16(a)に示すように、鳥瞰視画像において他車両VXのタイヤゴム部分に設定された注目線Laがエッジ線であると判断されていたとする。この場合、注目線La上の鳥瞰視画像の輝度変化はなだらかなものとなる。これは、カメラ10により撮像された画像が鳥瞰視画像に視点変換されたことにより、他車両VXのタイヤが鳥瞰視画像内で引き延ばされたことによる。一方、図16(b)に示すように、鳥瞰視画像において路面に描かれた「50」という白色文字部分に設定された注目線Laがエッジ線であると誤判定されていたとする。この場合、注目線La上の鳥瞰視画像の輝度変化は起伏の大きいものとなる。これは、エッジ線上に、白色文字における輝度が高い部分と、路面等の輝度が低い部分とが混在しているからである。
As shown in FIG. 16A, it is assumed that the attention line La set in the tire rubber portion of the other vehicle VX is determined to be an edge line in the bird's-eye view image. In this case, the luminance change of the bird's-eye view image on the attention line La is gentle. This is because the tire of the other vehicle VX is extended in the bird's-eye view image by converting the image captured by the camera 10 into a bird's-eye view image. On the other hand, as shown in FIG. 16B, it is assumed that the attention line La set in the white character portion “50” drawn on the road surface in the bird's-eye view image is erroneously determined as an edge line. In this case, the brightness change of the bird's-eye view image on the attention line La has a large undulation. This is because a portion with high brightness in white characters and a portion with low brightness such as a road surface are mixed on the edge line.
以上のような注目線La上の輝度分布の相違に基づいて、立体物検出部37は、エッジ線が誤判定により検出されたものか否かを判定する。立体物検出部37は、エッジ線に沿った輝度変化が所定の閾値よりも大きい場合には、当該エッジ線が誤判定により検出されたものであると判定する。そして、当該エッジ線は、立体物の検出には使用しない。これにより、路面上の「50」といった白色文字や路肩の雑草等がエッジ線として判定されてしまい、立体物の検出精度が低下することを抑制する。
Based on the difference in luminance distribution on the attention line La as described above, the three-dimensional object detection unit 37 determines whether or not the edge line is detected by erroneous determination. When the luminance change along the edge line is larger than a predetermined threshold, the three-dimensional object detection unit 37 determines that the edge line is detected by erroneous determination. And the said edge line is not used for the detection of a solid object. Thereby, white characters such as “50” on the road surface, weeds on the road shoulder, and the like are determined as edge lines, and the detection accuracy of the three-dimensional object is prevented from being lowered.
具体的には、立体物検出部37は、下記数式4,5の何れかにより、エッジ線の輝度変化を算出する。このエッジ線の輝度変化は、実空間上における鉛直方向の評価値に相当する。下記数式4は、注目線La上のi番目の輝度値I(xi,yi)と、隣接するi+1番目の輝度値I(xi+1,yi+1)との差分の二乗の合計値によって輝度分布を評価する。下記数式5は、注目線La上のi番目の輝度値I(xi,yi)と、隣接するi+1番目の輝度値I(xi+1,yi+1)との差分の絶対値の合計値よって輝度分布を評価する。
[数4]
鉛直相当方向の評価値=Σ[{I(xi,yi)-I(xi+1,yi+1)}2]
[数5]
鉛直相当方向の評価値=Σ|I(xi,yi)-I(xi+1,yi+1)| Specifically, the three-dimensionalobject detection unit 37 calculates the luminance change of the edge line by any one of the following mathematical formulas 4 and 5. The luminance change of the edge line corresponds to the evaluation value in the vertical direction in the real space. Equation 4 below evaluates the luminance distribution by the sum of the squares of the differences between the i-th luminance value I (xi, yi) on the attention line La and the adjacent i + 1-th luminance value I (xi + 1, yi + 1). . Equation 5 below evaluates the luminance distribution by the sum of the absolute values of the differences between the i-th luminance value I (xi, yi) on the attention line La and the adjacent i + 1-th luminance value I (xi + 1, yi + 1). To do.
[Equation 4]
Evaluation value in the vertical equivalent direction = Σ [{I (xi, yi) −I (xi + 1, yi + 1)} 2 ]
[Equation 5]
Evaluation value in the vertical equivalent direction = Σ | I (xi, yi) −I (xi + 1, yi + 1) |
[数4]
鉛直相当方向の評価値=Σ[{I(xi,yi)-I(xi+1,yi+1)}2]
[数5]
鉛直相当方向の評価値=Σ|I(xi,yi)-I(xi+1,yi+1)| Specifically, the three-dimensional
[Equation 4]
Evaluation value in the vertical equivalent direction = Σ [{I (xi, yi) −I (xi + 1, yi + 1)} 2 ]
[Equation 5]
Evaluation value in the vertical equivalent direction = Σ | I (xi, yi) −I (xi + 1, yi + 1) |
なお、数式5に限らず、下記数式6のように、閾値t2を用いて隣接する輝度値の属性bを二値化して、当該二値化した属性bを全ての注目点Paについて総和してもよい。
[数6]
鉛直相当方向の評価値=Σb(xi,yi)
但し、|I(xi,yi)-I(xi+1,yi+1)|>t2のとき、
b(xi,yi)=1
上記以外のとき、
b(xi,yi)=0 In addition, not onlyFormula 5 but also Formula 6 below, the threshold value t2 is used to binarize the attribute b of the adjacent luminance value, and the binarized attribute b is summed for all the attention points Pa. Also good.
[Equation 6]
Evaluation value in the vertical equivalent direction = Σb (xi, yi)
However, when | I (xi, yi) −I (xi + 1, yi + 1) |> t2,
b (xi, yi) = 1
Other than the above
b (xi, yi) = 0
[数6]
鉛直相当方向の評価値=Σb(xi,yi)
但し、|I(xi,yi)-I(xi+1,yi+1)|>t2のとき、
b(xi,yi)=1
上記以外のとき、
b(xi,yi)=0 In addition, not only
[Equation 6]
Evaluation value in the vertical equivalent direction = Σb (xi, yi)
However, when | I (xi, yi) −I (xi + 1, yi + 1) |> t2,
b (xi, yi) = 1
Other than the above
b (xi, yi) = 0
注目点Paiの輝度値と参照点Priの輝度値との輝度差の絶対値が閾値t2よりも大きい場合、当該注目点Pa(xi,yi)の属性b(xi,yi)は‘1’となる。それ以外の関係である場合には、注目点Paiの属性b(xi,yi)は‘0’となる。この閾値t2は、注目線Laが同じ立体物上にないことを判定するために実験等によって予め設定されている。そして、立体物検出部37は、注目線La上の全注目点Paについての属性bを総和して、鉛直相当方向の評価値を求めて、エッジ線が正しいものかを判定する。
When the absolute value of the luminance difference between the luminance value of the attention point Pai and the luminance value of the reference point Pri is larger than the threshold value t2, the attribute b (xi, yi) of the attention point Pa (xi, yi) is “1”. Become. If the relationship is other than that, the attribute b (xi, yi) of the attention point Pai is '0'. This threshold value t2 is set in advance by an experiment or the like in order to determine that the attention line La is not on the same three-dimensional object. Then, the three-dimensional object detection unit 37 sums up the attributes b for all the attention points Pa on the attention line La, obtains an evaluation value in the vertical equivalent direction, and determines whether the edge line is correct.
次に、本実施形態に係るエッジ情報を利用した立体物検出方法について説明する。図17及び図18は、本実施形態に係る立体物検出方法の詳細を示すフローチャートである。なお、図17及び図18においては、便宜上、検出領域A1を対象とする処理について説明するが、検出領域A2についても同様の処理が実行される。
Next, a three-dimensional object detection method using edge information according to the present embodiment will be described. 17 and 18 are flowcharts showing details of the three-dimensional object detection method according to the present embodiment. In FIG. 17 and FIG. 18, for the sake of convenience, the processing for the detection area A1 will be described, but the same processing is executed for the detection area A2.
図17に示すように、まず、先ずステップS20において、計算機30は所定のルールに基づいて検出領域を設定する。この検出領域の設定手法については後に詳述する。そして、ステップS21において、カメラ10は、画角a及び取付位置によって特定された所定領域を撮像する。次に視点変換部31は、ステップS22において、ステップS21にてカメラ10により撮像された撮像画像データを入力し、視点変換を行って鳥瞰視画像データを生成する。
As shown in FIG. 17, first, in step S20, the computer 30 sets a detection area based on a predetermined rule. This detection area setting method will be described in detail later. In step S21, the camera 10 captures an image of a predetermined area specified by the angle of view a and the attachment position. Next, in step S22, the viewpoint conversion unit 31 inputs the captured image data captured by the camera 10 in step S21, performs viewpoint conversion, and generates bird's-eye view image data.
次に輝度差算出部35は、ステップS23において、検出領域A1上に注目線Laを設定する。このとき、輝度差算出部35は、実空間上において鉛直方向に伸びる線に相当する線を注目線Laとして設定する。次に輝度差算出部35は、ステップS24において、検出領域A1上に参照線Lrを設定する。このとき、輝度差算出部35は、実空間上において鉛直方向に伸びる線分に該当し、且つ、注目線Laと実空間上において所定距離離れた線を参照線Lrとして設定する。
Next, in step S23, the luminance difference calculation unit 35 sets the attention line La on the detection area A1. At this time, the luminance difference calculation unit 35 sets a line corresponding to a line extending in the vertical direction in the real space as the attention line La. Next, the brightness | luminance difference calculation part 35 sets the reference line Lr on detection area | region A1 in step S24. At this time, the luminance difference calculation unit 35 sets a reference line Lr that corresponds to a line segment extending in the vertical direction in the real space and is separated from the attention line La by a predetermined distance in the real space.
次に輝度差算出部35は、ステップS25において、注目線La上に複数の注目点Paを設定する。この際に、輝度差算出部35は、エッジ線検出部36によるエッジ検出時に問題とならない程度の数の注目点Paを設定する。また、輝度差算出部35は、ステップS26において、実空間上において注目点Paと参照点Prとが略同じ高さとなるように、参照点Prを設定する。これにより、注目点Paと参照点Prとが略水平方向に並ぶこととなり、実空間上において鉛直方向に伸びるエッジ線を検出しやすくなる。
Next, in step S25, the luminance difference calculation unit 35 sets a plurality of attention points Pa on the attention line La. At this time, the luminance difference calculation unit 35 sets the attention points Pa as many as not causing a problem at the time of edge detection by the edge line detection unit 36. In step S26, the luminance difference calculation unit 35 sets the reference point Pr so that the attention point Pa and the reference point Pr are substantially the same height in the real space. Thereby, the attention point Pa and the reference point Pr are arranged in a substantially horizontal direction, and it becomes easy to detect an edge line extending in the vertical direction in the real space.
次に輝度差算出部35は、ステップS27において、実空間上において同じ高さとなる注目点Paと参照点Prとの輝度差を算出する。次にエッジ線検出部36は、上記の数式1に従って、各注目点Paの属性sを算出する。次にエッジ線検出部36は、ステップS28において、上記の数式2に従って、各注目点Paの属性sの連続性cを算出する。次にエッジ線検出部36は、ステップS29において、上記数式3に従って、連続性cの総和を正規化した値が閾値θより大きいか否かを判定する。正規化した値が閾値θよりも大きいと判断した場合(S29:YES)、エッジ線検出部36は、ステップS30において、当該注目線Laをエッジ線として検出する。そして、処理はステップS31に移行する。正規化した値が閾値θより大きくないと判断した場合(S29:NO)、エッジ線検出部36は、当該注目線Laをエッジ線として検出せず、処理はステップS31に移行する。
Next, in step S27, the luminance difference calculation unit 35 calculates the luminance difference between the attention point Pa and the reference point Pr that have the same height in the real space. Next, the edge line detection unit 36 calculates the attribute s of each attention point Pa in accordance with Equation 1 above. Next, in step S28, the edge line detection unit 36 calculates the continuity c of the attribute s of each attention point Pa in accordance with Equation 2 above. Next, in step S29, the edge line detection unit 36 determines whether or not the value obtained by normalizing the total sum of continuity c is greater than the threshold value θ according to the above formula 3. When it is determined that the normalized value is larger than the threshold θ (S29: YES), the edge line detection unit 36 detects the attention line La as an edge line in step S30. Then, the process proceeds to step S31. When it is determined that the normalized value is not larger than the threshold θ (S29: NO), the edge line detection unit 36 does not detect the attention line La as an edge line, and the process proceeds to step S31.
ステップS31において、計算機30は、検出領域A1上に設定可能な注目線Laの全てについて上記のステップS23~ステップS30の処理を実行したか否かを判断する。全ての注目線Laについて上記処理をしていないと判断した場合(S31:NO)、ステップS23に処理を戻して、新たに注目線Laを設定して、ステップS31までの処理を繰り返す。一方、全ての注目線Laについて上記処理をしたと判断した場合(S31:YES)、処理は図18のステップS32に移行する。
In step S31, the computer 30 determines whether or not the processing in steps S23 to S30 has been executed for all the attention lines La that can be set on the detection area A1. If it is determined that the above processing has not been performed for all the attention lines La (S31: NO), the processing returns to step S23, a new attention line La is set, and the processing up to step S31 is repeated. On the other hand, when it is determined that the above process has been performed for all the attention lines La (S31: YES), the process proceeds to step S32 in FIG.
図18のステップS32において、立体物検出部37は、図17のステップS30において検出された各エッジ線について、当該エッジ線に沿った輝度変化を算出する。立体物検出部37は、上記数式4,5,6の何れかの式に従って、エッジ線の輝度変化を算出する。次に立体物検出部37は、ステップS33において、エッジ線のうち、輝度変化が所定の閾値よりも大きいエッジ線を除外する。すなわち、輝度変化の大きいエッジ線は正しいエッジ線ではないと判定し、エッジ線を立体物の検出には使用しない。これは、上述したように、検出領域A1に含まれる路面上の文字や路肩の雑草等がエッジ線として検出されてしまうことを抑制するためである。したがって、所定の閾値とは、予め実験等によって求められた、路面上の文字や路肩の雑草等によって発生する輝度変化に基づいて設定された値となる。
In step S32 of FIG. 18, the three-dimensional object detection unit 37 calculates a luminance change along the edge line for each edge line detected in step S30 of FIG. The three-dimensional object detection unit 37 calculates the luminance change of the edge line according to any one of the above formulas 4, 5, and 6. Next, in step S33, the three-dimensional object detection unit 37 excludes edge lines whose luminance change is larger than a predetermined threshold from the edge lines. That is, it is determined that an edge line having a large luminance change is not a correct edge line, and the edge line is not used for detecting a three-dimensional object. As described above, this is to prevent characters on the road surface, roadside weeds, and the like included in the detection area A1 from being detected as edge lines. Therefore, the predetermined threshold value is a value set based on a luminance change generated by characters on the road surface, weeds on the road shoulder, or the like obtained in advance by experiments or the like.
次に立体物検出部37は、ステップS34において、エッジ線の量が第2閾値β以上であるか否かを判断する。例えば、検出対象の立体物として四輪車を設定した場合、当該第2閾値βは、予め実験等によって検出領域A1内において出現した四輪車のエッジ線の数に基づいて設定される。エッジ線の量が第2閾値β以上であると判定した場合(S34:YES)、立体物検出部37は、ステップS35において、検出領域A1内に立体物が存在すると検出する。一方、エッジ線の量が第2閾値β以上ではないと判定した場合(S34:NO)、立体物検出部37は、検出領域A1内に立体物が存在しないと判断する(S38)。検出された立体物は、自車両Vが走行する車線の隣の隣接車線を走行する他車両VXであると判断してもよいし、検出した立体物の自車両Vに対する相対速度を考慮して隣接車線を走行する他車両VXであるか否かを判断してもよい。具体的に、ステップS35において立体物が検出された場合には、ステップS36へ進み、その立体物の移動速度が所定値範囲内であるか否かを判断する。ステップS35で検出された立体物が隣接車線を走行する他車両であるか否かを判断するためである。特に手法は限定されないが、立体物の移動速度は画像上のエッジの移動速度に基づいて求めることができる。ステップS36において、ステップS35で検出された立体物の移動速度が所定値以内であれば、ステップS37へ進み、立体物を他車両と判断する。他方、ステップS36で、立体物の移動速度が所定値域を超える(範囲外である)場合には、ステップS39へ進み、立体物は他車両ではないと判断する。立体物が他車両であるか否かを判断する際の移動速度の所定値域は、先に図12の処理において説明したように、絶対移動速度が10km/h以上、且つ、立体物の自車両Vに対する相対移動速度が+60km/h以下とすることができる。移動速度の所定値域としては、絶対移動速度がプラスの値であること(マイナスの値でないこと)や、0km/hでないことを定義してもよい。その後、図17及び図18に示す処理は終了する。
Next, in step S34, the three-dimensional object detection unit 37 determines whether or not the amount of the edge line is equal to or larger than the second threshold value β. For example, when a four-wheeled vehicle is set as the three-dimensional object to be detected, the second threshold value β is set based on the number of edge lines of the four-wheeled vehicle that have appeared in the detection region A1 in advance through experiments or the like. When it is determined that the amount of the edge line is equal to or larger than the second threshold value β (S34: YES), the three-dimensional object detection unit 37 detects that a three-dimensional object exists in the detection area A1 in step S35. On the other hand, when it is determined that the amount of the edge line is not equal to or greater than the second threshold value β (S34: NO), the three-dimensional object detection unit 37 determines that there is no three-dimensional object in the detection area A1 (S38). The detected three-dimensional object may be determined to be another vehicle VX that travels in the adjacent lane adjacent to the lane in which the host vehicle V travels, and the relative speed of the detected three-dimensional object with respect to the host vehicle V is taken into consideration. It may be determined whether the vehicle is another vehicle VX traveling in the adjacent lane. Specifically, when a three-dimensional object is detected in step S35, the process proceeds to step S36, and it is determined whether or not the moving speed of the three-dimensional object is within a predetermined value range. This is because it is determined whether or not the three-dimensional object detected in step S35 is another vehicle traveling in the adjacent lane. Although the method is not particularly limited, the moving speed of the three-dimensional object can be obtained based on the moving speed of the edge on the image. In step S36, if the moving speed of the three-dimensional object detected in step S35 is within a predetermined value, the process proceeds to step S37, and the three-dimensional object is determined as another vehicle. On the other hand, when the moving speed of the three-dimensional object exceeds the predetermined value range (out of the range) in step S36, the process proceeds to step S39, and it is determined that the three-dimensional object is not another vehicle. The predetermined range of the moving speed when determining whether or not the three-dimensional object is another vehicle is that the absolute moving speed is 10 km / h or more and the own vehicle of the three-dimensional object as described in the processing of FIG. The relative movement speed with respect to V can be +60 km / h or less. As the predetermined value range of the moving speed, it may be defined that the absolute moving speed is a positive value (not a negative value) or not 0 km / h. Thereafter, the processing illustrated in FIGS. 17 and 18 ends.
本実施形態では、立体物が検出領域内に存在するか否かを判断する処理の後に立体物の移動速度(移動距離)を算出する処理を行う例を示すが、この順序は特に限定されない。つまり、立体物の移動速度が所定値域内である場合に、エッジ情報に基づいて立体物の検出処理を行ってもよい。また、立体物の検出(存否)処理と立体物の移動速度の算出処理を同時に並行して行ってもよい。
In the present embodiment, an example of performing a process of calculating a moving speed (movement distance) of a three-dimensional object after a process of determining whether or not a three-dimensional object exists in the detection region is shown, but this order is not particularly limited. That is, when the moving speed of the three-dimensional object is within the predetermined value range, the three-dimensional object detection process may be performed based on the edge information. In addition, the three-dimensional object detection (presence / absence) process and the three-dimensional object movement speed calculation process may be performed simultaneously in parallel.
以上のように、本実施形態のエッジ情報を利用した立体物の検出方法によれば、検出領域A1,A2に存在する立体物を検出するために、鳥瞰視画像に対して実空間において鉛直方向に伸びる線分としての鉛直仮想線を設定する。そして、鉛直仮想線に沿った複数の位置ごとに、当該各位置の近傍の2つの画素の輝度差を算出し、当該輝度差の連続性に基づいて立体物の有無を判定することができる。
As described above, according to the three-dimensional object detection method using the edge information of the present embodiment, in order to detect the three-dimensional object existing in the detection areas A1 and A2, the vertical direction in the real space with respect to the bird's-eye view image A vertical imaginary line is set as a line segment extending to. Then, for each of a plurality of positions along the vertical imaginary line, a luminance difference between two pixels in the vicinity of each position can be calculated, and the presence or absence of a three-dimensional object can be determined based on the continuity of the luminance difference.
具体的には、鳥瞰視画像における検出領域A1,A2に対して、実空間において鉛直方向に伸びる線分に該当する注目線Laと、注目線Laとは異なる参照線Lrとを設定する。そして、注目線La上の注目点Paと参照線Lr上の参照点Prとの輝度差を注目線La及び参照線Laに沿って連続的に求める。このように、点同士の輝度差を連続的に求めることにより、注目線Laと参照線Lrとの輝度差を求める。注目線Laと参照線Lrとの輝度差が高い場合には、注目線Laの設定箇所に立体物のエッジがある可能性が高い。これによって、連続的な輝度差に基づいて立体物を検出することができる。特に、実空間において鉛直方向に伸びる鉛直仮想線同士との輝度比較を行うために、鳥瞰視画像に変換することによって立体物が路面からの高さに応じて引き伸ばされてしまっても、立体物の検出処理が影響されることはない。したがって、本例の方法によれば、立体物の検出精度を向上させることができる。
Specifically, the attention line La corresponding to the line segment extending in the vertical direction in the real space and the reference line Lr different from the attention line La are set for the detection areas A1 and A2 in the bird's-eye view image. Then, a luminance difference between the attention point Pa on the attention line La and the reference point Pr on the reference line Lr is continuously obtained along the attention line La and the reference line La. In this way, the luminance difference between the attention line La and the reference line Lr is obtained by continuously obtaining the luminance difference between the points. In the case where the luminance difference between the attention line La and the reference line Lr is high, there is a high possibility that there is an edge of the three-dimensional object at the set position of the attention line La. Thereby, a three-dimensional object can be detected based on a continuous luminance difference. In particular, in order to compare brightness with vertical virtual lines extending in the vertical direction in real space, even if the three-dimensional object is stretched according to the height from the road surface by converting it to a bird's-eye view image, This detection process is not affected. Therefore, according to the method of this example, the detection accuracy of a three-dimensional object can be improved.
また、本例では、鉛直仮想線付近の略同じ高さの2つの点の輝度差を求める。具体的には、実空間上で略同じ高さとなる注目線La上の注目点Paと参照線Lr上の参照点Prとから輝度差を求めるので、鉛直方向に伸びるエッジが存在する場合における輝度差を明確に検出することができる。
Also, in this example, the luminance difference between two points of approximately the same height near the vertical imaginary line is obtained. Specifically, the luminance difference is obtained from the attention point Pa on the attention line La and the reference point Pr on the reference line Lr, which are substantially the same height in the real space, and thus the luminance when there is an edge extending in the vertical direction. The difference can be detected clearly.
更に、本例では、注目線La上の注目点Paと参照線Lr上の参照点Prとの輝度差に基づいて注目点Paに属性付けを行い、注目線Laに沿った属性の連続性cに基づいて当該注目線Laがエッジ線であるかを判断するので、輝度の高い領域と輝度の低い領域との境界をエッジ線として検出し、人間の自然な感覚に沿ったエッジ検出を行うことができる。この効果について詳細に説明する。図19は、エッジ線検出部36の処理を説明する画像例を示す図である。この画像例は、輝度の高い領域と輝度の低い領域とが繰り返される縞模様を示す第1縞模様101と、輝度の低い領域と輝度の高い領域とが繰り返される縞模様を示す第2縞模様102とが隣接した画像である。また、この画像例は、第1縞模様101の輝度が高い領域と第2縞模様102の輝度の低い領域とが隣接すると共に、第1縞模様101の輝度が低い領域と第2縞模様102の輝度が高い領域とが隣接している。この第1縞模様101と第2縞模様102との境界に位置する部位103は、人間の感覚によってはエッジとは知覚されない傾向にある。
Furthermore, in this example, the attention point Pa is attributed based on the luminance difference between the attention point Pa on the attention line La and the reference point Pr on the reference line Lr, and attribute continuity c along the attention line La is obtained. Therefore, it is determined whether the attention line La is an edge line. Therefore, the boundary between the high luminance area and the low luminance area is detected as an edge line, and edge detection is performed in accordance with a natural human sense. Can do. This effect will be described in detail. FIG. 19 is a diagram illustrating an example of an image for explaining the processing of the edge line detection unit 36. In this image example, a first striped pattern 101 showing a striped pattern in which a high brightness area and a low brightness area are repeated, and a second striped pattern showing a striped pattern in which a low brightness area and a high brightness area are repeated. 102 is an adjacent image. Further, in this image example, a region where the brightness of the first striped pattern 101 is high and a region where the brightness of the second striped pattern 102 is low are adjacent to each other, and a region where the brightness of the first striped pattern 101 is low and the second striped pattern 102. Is adjacent to a region with high brightness. The portion 103 located at the boundary between the first striped pattern 101 and the second striped pattern 102 tends not to be perceived as an edge depending on human senses.
これに対し、輝度の低い領域と輝度が高い領域とが隣接しているために、輝度差のみでエッジを検出すると、当該部位103はエッジとして認識されてしまう。しかし、エッジ線検出部36は、部位103における輝度差に加えて、当該輝度差の属性に連続性がある場合にのみ部位103をエッジ線として判定するので、エッジ線検出部36は、人間の感覚としてエッジ線として認識しない部位103をエッジ線として認識してしまう誤判定を抑制でき、人間の感覚に沿ったエッジ検出を行うことができる。
On the other hand, since the low luminance region and the high luminance region are adjacent to each other, if the edge is detected only by the luminance difference, the portion 103 is recognized as an edge. However, since the edge line detection unit 36 determines the part 103 as an edge line only when there is continuity in the attribute of the luminance difference in addition to the luminance difference in the part 103, the edge line detection unit 36 An erroneous determination of recognizing a part 103 that is not recognized as an edge line as a sensation as an edge line can be suppressed, and edge detection according to a human sensation can be performed.
さらに、本例では、エッジ線検出部36により検出されたエッジ線の輝度変化が所定の閾値よりも大きい場合には、当該エッジ線が誤判定により検出されたものと判断する。カメラ10により取得された撮像画像を鳥瞰視画像に変換した場合、当該撮像画像に含まれる立体物は、引き伸ばされた状態で鳥瞰視画像に現れる傾向がある。例えば、上述したように他車両VXのタイヤが引き伸ばされた場合に、タイヤという1つの部位が引き伸ばされるため、引き伸ばされた方向における鳥瞰視画像の輝度変化は小さい傾向となる。これに対し、路面に描かれた文字等をエッジ線として誤判定した場合に、鳥瞰視画像には、文字部分といった輝度が高い領域と路面部分といった輝度が低い領域とが混合されて含まれる。この場合に、鳥瞰視画像において、引き伸ばされた方向の輝度変化は大きくなる傾向がある。したがって、本例のようにエッジ線に沿った鳥瞰視画像の輝度変化を判定することによって、誤判定により検出されたエッジ線を認識することができ、立体物の検出精度を高めることができる。
Furthermore, in this example, when the luminance change of the edge line detected by the edge line detection unit 36 is larger than a predetermined threshold value, it is determined that the edge line has been detected by erroneous determination. When the captured image acquired by the camera 10 is converted into a bird's-eye view image, the three-dimensional object included in the captured image tends to appear in the bird's-eye view image in a stretched state. For example, as described above, when the tire of the other vehicle VX is stretched, since one portion of the tire is stretched, the luminance change of the bird's-eye view image in the stretched direction tends to be small. On the other hand, when a character or the like drawn on the road surface is erroneously determined as an edge line, the bird's-eye view image includes a high luminance region such as a character portion and a low luminance region such as a road surface portion. In this case, the brightness change in the stretched direction tends to increase in the bird's-eye view image. Therefore, by determining the luminance change of the bird's-eye view image along the edge line as in this example, the edge line detected by the erroneous determination can be recognized, and the detection accuracy of the three-dimensional object can be improved.
立体物検出部33,37は、さらに乗員への報知や車両制御のため、検出結果を外部の車両コントローラへ送出する。なお、本例の立体物検出装置1は、上述した2つの立体物検出部33(又は立体物検出部37)により検出された立体物について、さらに検証して立体物を検出対象物である他車両VXであるか否かを最終的に判断する判断部を備えることもできる。
The three-dimensional object detection units 33 and 37 send detection results to an external vehicle controller for further notification to the occupant and vehicle control. Note that the three-dimensional object detection device 1 of the present example is a three-dimensional object detected by the above-described two three-dimensional object detection units 33 (or the three-dimensional object detection unit 37), and the three-dimensional object is a detection target. A determination unit that finally determines whether the vehicle is a vehicle VX can also be provided.
ここで、本実施形態の検出領域設定部34の処理を具体的に説明する。先述したように、検出領域設定部34は、検出領域A1,A2のそれぞれについて面積の異なる第1検出領域と第2検出領域とを有する。具体的に、検出領域設定部34は、立体物検出部33,37が検出領域A1,A2に存在する立体物の移動距離を算出する場合には第1検出領域A11,A21を設定し、立体物検出部33,37が検出領域A1,A2に存在する立体物を検出する場合には第1検出領域A11,A21よりも面積が小さい第2検出領域A12,A22を設定する。
Here, the processing of the detection area setting unit 34 of the present embodiment will be specifically described. As described above, the detection region setting unit 34 includes the first detection region and the second detection region having different areas for the detection regions A1 and A2. Specifically, the detection region setting unit 34 sets the first detection regions A11 and A21 when the three-dimensional object detection units 33 and 37 calculate the movement distance of the three-dimensional object existing in the detection regions A1 and A2, and the three-dimensional object detection unit 33 and 37 sets the first detection regions A11 and A21. When the object detection units 33 and 37 detect solid objects existing in the detection areas A1 and A2, the second detection areas A12 and A22 having a smaller area than the first detection areas A11 and A21 are set.
本実施形態において、検出領域設定部34は、検出領域A1,A2のそれぞれについて、所定の第1検出領域と、この第1検出領域のうちカメラ10の設置位置から車両の側方方向の距離が所定距離以上の領域、つまり第1検出領域の外側(路肩側)の帯状の領域を欠損させた第2検出領域とを有する。第2検出領域は、第1検出領域の路肩側の辺を車両側に移動させ、検出領域の幅を車幅方向に狭めた領域である。本実施形態の第1検出領域は、デフォルトの検出領域と共通する領域として定義してもよい。この検出領域の態様については、図22に基づいて後述する。
In the present embodiment, the detection area setting unit 34 has a predetermined first detection area and a distance in the lateral direction of the vehicle from the installation position of the camera 10 in the first detection area for each of the detection areas A1 and A2. And a second detection region in which a band-like region outside the first detection region (on the shoulder side) is lost. The second detection area is an area in which the roadside side of the first detection area is moved to the vehicle side and the width of the detection area is narrowed in the vehicle width direction. The first detection area of the present embodiment may be defined as an area common to the default detection area. The mode of this detection area will be described later with reference to FIG.
本実施形態に係る立体物検出装置1は、車載カメラ10により取得した車両後方の撮像画像の画像情報に基づいて立体物の存在の検出及び移動速度の検出をする。自車両Vの走行車線の隣の隣接車線を走行する他車両VXを障害物として検出する際に、自車両の走行車線の路肩に設けられた植え込みやガードレール等の物体の映像を誤って隣接車線を走行する他車両の映像として誤認するという問題がある。図20に示すように、隣接車線の路肩に一定間隔で植えつけられた植栽Q1やグリーンベルトの一面に生えている草Q1などが存在する場合や、路肩に同じ形状のガードレールQ2や縁石Q2が存在する場合においては、これらの植栽・草Q1やガードレール・縁石Q2の映像が検出領域内に映り込んでしまう場合がある。植栽・草Q1は自然物であるため形状が一定ではないが、カメラ10で周期的に撮像すると別の物体であるにもかかわらず、同一の物体のような特徴点が画像情報に含まれてしまう場合がある。また、植栽・草Q1の形状が一定でないがゆえに、画像情報に含まれる特徴点が偶然にも他車両VXの特徴点と似てしまう場合がある。さらに、ガードレール・縁石Q2は、同じ形状のものが繰り返し配置されるため、カメラ10の撮像周期、自車両Vの車速、ガードレール・縁石Q2の繰り返しパターンの単位長さの組み合わせによって、異なるガードレール・縁石Q2の画像情報が同一物体のような特徴点を示してしまう場合や、ガードレール・縁石Q2の画像情報に含まれる特徴点が偶然にも他車両VXの特徴点と似てしまう場合がある。このように、画像情報の特徴が他車両VXの特徴と似てしまうと、自車両Vの走行車線の路肩に設けられた植栽・草Q1やガードレール・縁石Q2などの検出対象以外の物体の映像を誤って隣接車線を走行する他車両VXの映像として誤認してしまう場合がある。特に、自車両の走行車線が狭い場合には、路肩の物体の映像が隣接車線に対応する領域に入ってしまうため、上記問題が起きやすい。加えて、画像を視点変換して鳥瞰視画像とすると、画像が横に広がって路肩の物体の映像が検出領域A1,A2内に入り込み、上記問題が起きやすい。
The three-dimensional object detection device 1 according to the present embodiment detects the presence of a three-dimensional object and the moving speed based on image information of a captured image behind the vehicle acquired by the in-vehicle camera 10. When another vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the host vehicle V is detected as an obstacle, an image of an object such as a planting or guardrail provided on the shoulder of the traveling lane of the host vehicle is erroneously displayed in the adjacent lane. There is a problem that it is mistaken as an image of another vehicle traveling on the road. As shown in FIG. 20, when there is planting Q1 planted at a certain interval on the shoulder of an adjacent lane or grass Q1 growing on one side of the green belt, or the guard rail Q2 or curb stone Q2 having the same shape on the shoulder In the case where there is, there are cases where the images of the planting / grass Q1 and the guardrail / curbstone Q2 are reflected in the detection area. Although the shape of planting / grass Q1 is not constant because it is a natural object, the image information includes feature points like the same object even though it is a different object when periodically photographed by the camera 10. May end up. In addition, since the shape of the planting / grass Q1 is not constant, the feature points included in the image information may accidentally resemble the feature points of the other vehicle VX. Further, since the guard rail / curbstone Q2 having the same shape is repeatedly arranged, the guardrail / curbstone differs depending on the combination of the imaging period of the camera 10, the vehicle speed of the host vehicle V, and the unit length of the repeated pattern of the guardrail / curbstone Q2. The image information of Q2 may indicate a feature point like the same object, or the feature point included in the image information of the guardrail / curbstone Q2 may accidentally resemble the feature point of the other vehicle VX. As described above, when the characteristics of the image information are similar to the characteristics of the other vehicle VX, the objects other than the detection target such as the planting / grass Q1 or the guardrail / curbstone Q2 provided on the shoulder of the traveling lane of the own vehicle V There is a case where the video is mistakenly mistaken as the video of the other vehicle VX traveling in the adjacent lane. In particular, when the traveling lane of the host vehicle is narrow, the image of the object on the road shoulder enters the region corresponding to the adjacent lane, and thus the above problem is likely to occur. In addition, when the viewpoint of the image is converted into a bird's-eye view image, the image spreads horizontally and the image of the object on the road shoulder enters the detection areas A1 and A2, and the above problem is likely to occur.
このため、本実施形態の立体物検出装置1は、各検出領域A1,A2のそれぞれについて面積の異なる二つの検出領域A11,A12,A21,A22を準備し、処理の特性に応じて上記検出領域を切り替える。この場合において、検出領域設定部34は、立体物検出部33,37が立体物の移動距離を算出する場合には相対的に広い第1検出領域A11,A21を設定し、立体物検出部33,37が検出領域A1,A2に存在する立体物を検出する場合には第1検出領域A11,A21よりも面積が小さい第2検出領域A12,A22を設定する。自車両Vは移動しており、立体物(他車両VXを含む)も移動する場合があるので、立体物の速度(時間あたりの移動距離)の算出は広い範囲で行わないと期待する精度の検出結果を得ることができない。このため、立体物の移動速度を検出するためには、検出領域A1,A2を所定値以上の面積で設定する必要がある。特に、立体物の移動速度を検出する場合の検出領域A1,A2は自車両Vの進行方向に沿って所定値以上の距離を設定する必要がある。他方、道路の路肩の植栽、草、ガードレール、縁石などの像が画像情報に含まれている場合の影響を排除するためには、検出領域A1,A2を狭く設定する必要がある。特に、立体物の移動速度を検出する場合の検出領域A1,A2は自車両Vの進行方向に沿って所定値未満の距離とする、又は検出領域A1,A2は自車両Vの車幅方向に沿って所定値未満の距離とすることが好ましい。この二つの第1検出領域A11,A21及び第2検出領域A12,A22を処理場面に応じて設定することにより、立体物の移動速度を正確に把握しつつ、道路の路肩の植栽、草、ガードレール、縁石などを他車両VXとして誤検出することなく、他車両VXを高い精度で検出することができる。なお、第1検出領域A11と第2検出領域A12、又は第1検出領域A21と第2検出領域A22とは、同じ面積、同じ形態であってもよいし、異なる面積、異なる形態であってもよい。
For this reason, the three-dimensional object detection device 1 according to the present embodiment prepares two detection areas A11, A12, A21, and A22 having different areas for each of the detection areas A1 and A2, and the detection areas according to the processing characteristics. Switch. In this case, the detection area setting unit 34 sets the relatively wide first detection areas A11 and A21 when the three-dimensional object detection units 33 and 37 calculate the movement distance of the three-dimensional object, and the three-dimensional object detection unit 33. , 37 detect the three-dimensional object existing in the detection areas A1 and A2, the second detection areas A12 and A22 having a smaller area than the first detection areas A11 and A21 are set. Since the host vehicle V is moving and solid objects (including other vehicles VX) may also move, the calculation of the speed (movement distance per hour) of the three-dimensional object is not expected to be performed over a wide range. The detection result cannot be obtained. For this reason, in order to detect the moving speed of the three-dimensional object, it is necessary to set the detection areas A1 and A2 with an area of a predetermined value or more. In particular, the detection areas A1 and A2 when detecting the moving speed of the three-dimensional object need to set a distance of a predetermined value or more along the traveling direction of the host vehicle V. On the other hand, it is necessary to set the detection areas A1 and A2 to be narrow in order to eliminate the influence when the image information includes images of roadside shoulder planting, grass, guardrails, curbs, and the like. In particular, the detection areas A1 and A2 when detecting the moving speed of the three-dimensional object are set to a distance less than a predetermined value along the traveling direction of the host vehicle V, or the detection areas A1 and A2 are in the vehicle width direction of the host vehicle V. It is preferable that the distance is less than a predetermined value along. By setting these two first detection areas A11, A21 and second detection areas A12, A22 according to the processing scene, while accurately grasping the moving speed of the three-dimensional object, planting the road shoulder, grass, The other vehicle VX can be detected with high accuracy without erroneously detecting a guardrail, a curbstone, or the like as the other vehicle VX. The first detection region A11 and the second detection region A12, or the first detection region A21 and the second detection region A22 may have the same area and the same form, or may have different areas and different forms. Good.
以下、図21~図24に基づいて、検出領域A11,A12,A21,A22の具体的な態様を説明する。
Hereinafter, specific modes of the detection areas A11, A12, A21, and A22 will be described with reference to FIGS.
図21~24に示すように、本実施形態の検出領域A1は、第1検出領域A11と第2検出領域A12を有する。なお、以下の説明では検出領域A1を例にして説明するが、検出領域A2の第1検出領域A21と第2検出領域A22についても同様である。また、図21~図24において、カメラ10からの後方距離とは車長方向の図中+y方向に沿う距離であり、カメラ10からの側方距離とは、車幅方向の図中+xに沿う距離である。検出領域A11,A12,A21,A22は、検出領域設定部34により設定される検出領域の一態様であり、車両後方の右側、左側に設定されるものであれば特に限定されない。立体物検出装置の検出領域設定部34は、例えばデフォルトで設定する所定の検出領域と同じ第1検出領域A11,A21、第2検出領域A21,A22を設定してもよいし、例えばデフォルトで設定する所定の検出領域に内包される第1検出領域A11,A21、第2検出領域A21,A22を設定してもよい。
21 to 24, the detection area A1 of the present embodiment has a first detection area A11 and a second detection area A12. In the following description, the detection area A1 will be described as an example, but the same applies to the first detection area A21 and the second detection area A22 of the detection area A2. 21 to 24, the rear distance from the camera 10 is a distance along the + y direction in the vehicle length direction, and the lateral distance from the camera 10 is along the + x in the vehicle width direction. Distance. The detection areas A11, A12, A21, and A22 are one aspect of the detection area set by the detection area setting unit 34, and are not particularly limited as long as they are set on the right side and the left side behind the vehicle. The detection area setting unit 34 of the three-dimensional object detection device may set the first detection areas A11 and A21 and the second detection areas A21 and A22 that are the same as the predetermined detection area set by default, for example, set by default. The first detection areas A11 and A21 and the second detection areas A21 and A22 included in the predetermined detection area may be set.
図21に示すように、本例の第2検出領域A12は、第1検出領域11のうちカメラ10の位置からの後方距離(+y方向の距離)が所定距離以上の領域A13を欠損させた(除去した)領域である。同図に示すように、自車両Vの進行方向に沿う第2検出領域A12の長さD2は、第1検出領域A11の長さD1よりも短い。長さD2とD1との関係は、検知領域A1,A2の大きさ等に応じて適宜に設定することができる。このように、第2検出領域A12を、第1検出領域A11のうちカメラ10からの後方距離が所定距離以上の領域A13を欠損させた領域とすることにより、路肩の植栽、草、ガードレール、縁石などの映像から周期的な特徴を抽出しにくくして、同一の物体のような特徴点が画像情報に含まれてしまう確率、及び画像情報に含まれる特徴点が他車両VXの特徴点と似てしまう確率を低下させることができる。
As shown in FIG. 21, in the second detection area A12 of this example, the area A13 in which the rear distance (the distance in the + y direction) from the position of the camera 10 in the first detection area 11 is a predetermined distance or more is lost ( (Removed) area. As shown in the figure, the length D2 of the second detection area A12 along the traveling direction of the host vehicle V is shorter than the length D1 of the first detection area A11. The relationship between the lengths D2 and D1 can be appropriately set according to the size of the detection areas A1 and A2. Thus, by setting the second detection region A12 as a region in which the rear distance from the camera 10 is a predetermined distance or more in the first detection region A11, planting of the road shoulder, grass, guardrail, It is difficult to extract periodic features from images such as curbs, the probability that feature points like the same object will be included in the image information, and the feature points included in the image information are the feature points of the other vehicle VX. The probability of being similar can be reduced.
次に、図22に示すように、本例の第2検出領域A12は、第1検出領域A11のうちカメラ10の位置からの側方距離(-x方向の距離)が所定距離以上の領域A13を欠損させた領域である。つまり、第2検出領域A12,A21は、第1検出領域A11,A21の外側(車両から離隔する側)の領域が除かれた領域である。同図に示すように、自車両Vの車幅方向に沿う第2検出領域A12の長さW2は、第1検出領域A11の長さW1よりも短い。長さW2とW1との関係は、検知領域A1,A2の大きさ等に応じて適宜に設定することができる。この態様において、図23に示すように、カメラ10の位置からの側方距離の所定距離は、カメラ10から離隔するに従いW3からW2、さらにW1と次第に大きくすることができる。このように、第2検出領域A12を第1検出領域11のうちカメラ10からの側方距離が所定距離以上の領域A13を欠損させた領域とすることにより、路肩の植栽、草、ガードレール、縁石などの映像をマスクして、これらが検出処理に用いられる画像情報に含まれないようにすることができるので、そもそも、路肩の植栽、草、ガードレール、縁石などについて立体物であるか否かの判断をしないようにすることができる。
Next, as shown in FIG. 22, the second detection area A12 of this example is an area A13 in which the lateral distance (distance in the −x direction) from the position of the camera 10 in the first detection area A11 is a predetermined distance or more. This is a region where is missing. That is, the second detection areas A12 and A21 are areas where the areas outside the first detection areas A11 and A21 (the side away from the vehicle) are excluded. As shown in the figure, the length W2 of the second detection area A12 along the vehicle width direction of the host vehicle V is shorter than the length W1 of the first detection area A11. The relationship between the lengths W2 and W1 can be appropriately set according to the sizes of the detection areas A1 and A2. In this aspect, as shown in FIG. 23, the predetermined lateral distance from the position of the camera 10 can gradually increase from W3 to W2, and further to W1, as the distance from the camera 10 increases. Thus, by setting the second detection region A12 as a region in which the side distance from the camera 10 in the first detection region 11 is greater than or equal to a predetermined distance, planting roadsides, grass, guardrails, Since it is possible to mask images such as curbs so that they are not included in the image information used for detection processing, whether or not they are solid objects such as roadside planting, grass, guardrails, curbs, etc. It can be made not to judge.
さらに、図24に示すように、本例の第2検出領域A12は、第1検出領域11のうちカメラ10からの後方距離が所定距離以内かつカメラ10からの側方距離が所定距離以上である領域A13を欠損させた領域である。同図に示すように、自車両Vの進行方向に沿う第2検出領域A12の長さD2は、第1検出領域A11の長さD1よりも短く、かつ自車両Vの車幅方向に沿う第2検出領域A12の長さW2は、第1検出領域A11の長さW1よりも短い。D1.D2,W1,W2の関係は特に限定されず、検出領域A1,A2の大きさに応じて適宜に設定することができる。また、この態様において、欠損させる領域A13の面積は第1検出領域A11の外側前方の頂点E1が含まれていれば特に限定されず、W2、D2の距離は任意に設定することができる。
Furthermore, as shown in FIG. 24, in the second detection area A12 of this example, the rear distance from the camera 10 in the first detection area 11 is within a predetermined distance, and the lateral distance from the camera 10 is greater than or equal to the predetermined distance. This is a region where the region A13 is missing. As shown in the figure, the length D2 of the second detection region A12 along the traveling direction of the host vehicle V is shorter than the length D1 of the first detection region A11 and the second detection region A12 extends along the vehicle width direction of the host vehicle V. The length W2 of the second detection area A12 is shorter than the length W1 of the first detection area A11. D1. The relationship between D2, W1, and W2 is not particularly limited, and can be set as appropriate according to the sizes of the detection areas A1 and A2. Further, in this aspect, the area of the region A13 to be deleted is not particularly limited as long as the vertex E1 on the outer front side of the first detection region A11 is included, and the distance between W2 and D2 can be arbitrarily set.
異なる観点によれば、図24に示す車両後方の右側の検出領域A2に設定された第2検出領域A22は、同じ検出領域A2に設定された第1検出領域A21のうち車両前方側の右端に位置する頂点E2を含む領域A23を欠損させた領域であり、車両後方の左側の検出領域A1に設定された第2検出領域A12は、同じ検出領域A1に設定された第1検出領域A11のうち車両前方側の左端に位置する頂点E1を含む領域A13を欠損させた領域である。第1検出領域A11のうち欠損させる領域A13は、少なくとも頂点E1を含み,第1検出領域A21のうち欠損させるA23は少なくとも頂点E2を含むものであればよい。
According to a different viewpoint, the second detection area A22 set in the detection area A2 on the right side behind the vehicle shown in FIG. 24 is located at the right end on the vehicle front side in the first detection area A21 set in the same detection area A2. The second detection area A12 set in the detection area A1 on the left side of the rear of the vehicle is an area in which the area A23 including the vertex E2 that is positioned is lost, and is the first detection area A11 set in the same detection area A1 This is a region where the region A13 including the vertex E1 located at the left end on the vehicle front side is missing. The region A13 to be deleted in the first detection region A11 may include at least the vertex E1, and the A23 to be deleted in the first detection region A21 may include at least the vertex E2.
これにより、立体物の移動速度を正確に把握しつつも、道路の路肩の植栽、草、ガードレール、縁石などを他車両VXとして誤検出することなく、他車両VXを高い精度で検出することができる。
Thus, while accurately grasping the moving speed of the three-dimensional object, the other vehicle VX can be detected with high accuracy without erroneously detecting road shoulder planting, grass, guardrail, curbstone, etc. as the other vehicle VX. Can do.
さらに、検出領域設定部34は、自車両Vに接近する立体物を検出した場合には、第2検出領域A12,A22の面積を大きく拡張することができる。この図24に示す態様において、検出領域設定部34は、頂点E1を含む領域A13,頂点E2を含む領域A23を欠損させるものであれば、第2検出領域A12,A22の面積を第1検出領域A11,A21と同じ面積にすることができる。
Furthermore, when the detection area setting unit 34 detects a three-dimensional object approaching the host vehicle V, the detection area setting section 34 can greatly expand the areas of the second detection areas A12 and A22. In the aspect shown in FIG. 24, if the detection area setting unit 34 deletes the area A13 including the vertex E1 and the area A23 including the vertex E2, the areas of the second detection areas A12 and A22 are determined as the first detection area. The area can be the same as A11 and A21.
図25は、本処理を行う場合の制御手順である。図25に示す処理は、前回の立体物検出処理の後に、前回処理の結果を利用して行われる今回の立体検出処理である。つまり、先述したように、本実施形態の検出領域設定部34は、立体物の移動速度を検出する際には第1検出領域A11,A21の画像情報を利用し、立体物の存在を検出する際には第2検出領域A12,A22の画像情報を利用するように、各検出領域を設定する。図26に通常状態の第2検出領域A12,A22を示す。
FIG. 25 shows a control procedure when this processing is performed. The process illustrated in FIG. 25 is the current three-dimensional detection process performed using the result of the previous process after the previous three-dimensional object detection process. That is, as described above, the detection area setting unit 34 of the present embodiment detects the presence of a three-dimensional object using the image information of the first detection areas A11 and A21 when detecting the moving speed of the three-dimensional object. At this time, each detection area is set so as to use the image information of the second detection areas A12 and A22. FIG. 26 shows the second detection areas A12 and A22 in the normal state.
ステップS41において立体物が接近していることが検出された場合には、ステップS46に進む。そして、ステップS46において、相対的に面積の小さい第2検出領域A12,A22を拡張する。図27に拡張された第2検出領域A12´,A22´を示す。図26及び図27に示すように、第2検出領域A12,A22のカメラ10からの後方距離はD21からD21´に拡張されている。本例では、車両の進行方向(カメラ10の後方距離)に沿って第2検出領域A12,A22を拡張する例を示すが、図22に示す例のように車幅方向に小さい第2検出領域A12,A22が設定されている場合には、車幅方向の距離Wを伸ばして、第2検出領域A12,A22を拡張してもよい。もちろん、拡張した結果第1検出領域A11,A21と同じ面積及び形状としてもよい。
If it is detected in step S41 that the three-dimensional object is approaching, the process proceeds to step S46. In step S46, the second detection areas A12 and A22 having a relatively small area are expanded. FIG. 27 shows the expanded second detection areas A12 ′ and A22 ′. As shown in FIGS. 26 and 27, the rear distance from the camera 10 of the second detection areas A12 and A22 is extended from D21 to D21 ′. In this example, the second detection areas A12 and A22 are extended along the traveling direction of the vehicle (the rear distance of the camera 10), but the second detection area is small in the vehicle width direction as in the example shown in FIG. When A12 and A22 are set, the second detection areas A12 and A22 may be expanded by extending the distance W in the vehicle width direction. Of course, it is good also as the same area and shape as 1st detection area | region A11, A21 as a result of extending.
つぎに、ステップ42において、検出領域設定部34は、第1検出領域A11,A21と拡張された第2検出領域A12,A22を設定する。立体物(他車両VX)が自車両に接近している場合には、検出範囲を最大にして検出処理を行うことが好ましい。他車両が接近していない通常状態であれば、植栽やガードレールを他車両として誤検出することを防止することが優先されるが、他車両が接近している監視強化状態においては、誤検出防止よりも他車両を継続的に検出することが優先される。本実施形態では、立体物が接近している場合には、拡張された第2検出領域A12,A22を設定するので、立体物の検出を慎重に行うことができ、他車両VXの検出の精度を高めることができる。
Next, in step 42, the detection area setting unit 34 sets the first detection areas A11 and A21 and the extended second detection areas A12 and A22. When the three-dimensional object (other vehicle VX) is approaching the host vehicle, it is preferable to perform the detection process with the detection range being maximized. In the normal state where other vehicles are not approaching, priority is given to preventing false detection of planting and guardrails as other vehicles, but in the enhanced monitoring state where other vehicles are approaching, false detection Priority is given to continuously detecting other vehicles over prevention. In the present embodiment, when the three-dimensional object is approaching, the expanded second detection areas A12 and A22 are set, so that the three-dimensional object can be detected carefully and the accuracy of detection of the other vehicle VX is determined. Can be increased.
検出領域A1,A2が設定されたら、ステップS43に進み、立体物の検出処理を行う。この立体物の検出処理は上述した立体物検出部33による図11、図12の差分波形情報を用いた処理、又は立体物検出部37による図17、図18のエッジ情報を用いた処理に従って行われる。そして、ステップ43において、この立体物検出部33,37により検出領域A1,A2に立体物が検出された場合にはステップS45に進み、検出された立体物が他車両VXであると判断する。他方、立体物検出部33,37により検出領域A1,A2に立体物が検出されない場合にはステップS47に進み、検出領域A1,A2に他車両VXは存在しないと判断する。
If the detection areas A1 and A2 are set, the process proceeds to step S43 to perform a solid object detection process. This three-dimensional object detection processing is performed according to the above-described processing using the difference waveform information of FIGS. 11 and 12 by the three-dimensional object detection unit 33 or the processing using the edge information of FIGS. 17 and 18 by the three-dimensional object detection unit 37. Is called. In step 43, if a three-dimensional object is detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S45, and it is determined that the detected three-dimensional object is the other vehicle VX. On the other hand, when a solid object is not detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S47, and it is determined that no other vehicle VX exists in the detection areas A1 and A2.
図28は、他の制御手順を示すフローチャートである。図28に示す処理は、立体物の移動速度の算出結果を用いて検出領域を設定する処理である。立体物の移動速度は、前回の立体物検出処理における結果を利用して行ってもよいし、立体物検出処理と並行して行われる移動速度の算出処理の結果を利用して行ってもよい。本実施形態の検出領域設定部34は、立体物の移動速度を検出する際には第1検出領域A11,A21の画像情報を利用し、立体物の存在を検出する際には第2検出領域A12,A22の画像情報を利用する。先述した図22に通常状態の第2検出領域A12,A22、第1検出領域A11,A21を示す。
FIG. 28 is a flowchart showing another control procedure. The process illustrated in FIG. 28 is a process of setting a detection area using the calculation result of the moving speed of the three-dimensional object. The movement speed of the three-dimensional object may be performed using the result of the previous three-dimensional object detection process, or may be performed using the result of the movement speed calculation process performed in parallel with the three-dimensional object detection process. . The detection area setting unit 34 of the present embodiment uses the image information of the first detection areas A11 and A21 when detecting the moving speed of the three-dimensional object, and the second detection area when detecting the presence of the three-dimensional object. The image information of A12 and A22 is used. FIG. 22 described above shows the second detection areas A12 and A22 and the first detection areas A11 and A21 in the normal state.
ステップS51において、移動距離に基づいて算出された立体物の移動速度が所定閾値以内であり、立体物が他車両である可能性が高いと判断された場合には、ステップS46に進む。そして、ステップS46において、幅が狭められていた第2検出領域A12,A22を拡張する。
具体的に、立体物の移動速度が所定値以外である場合には、その立体物は他車両ではないと判断できる。このため、本実施形態では、立体物の検出の精度を向上させることを優先するために、第2検出領域A12,A22を、図22に示すように、第1検出領域A11,A21のうちカメラ10の設置位置から車両の側方方向の距離Wが所定距離W2以上の領域、つまり第1検出領域の外側(路肩側)の帯状の領域A13,A23を欠損させた(車幅方向に狭めた)領域とする。
他方、立体物の移動速度が所定値以内である場合には、その立体物が他車両である可能性が高いと考えられる。このため、第2検出領域A12,A22を、図29に示すように、第1検出領域A11,A21のうちカメラ10の設置位置から車両の側方方向の距離Wが所定距離W2+q(>W2)以上の領域を欠損させた領域とする。つまり欠損させる領域を小さくして、第2検出領域A12,A22を拡張する。もちろん、この場合の第2検出領域を、第1検出領域A11,A21と同じ面積及び形状としてもよい。 If it is determined in step S51 that the moving speed of the three-dimensional object calculated based on the moving distance is within the predetermined threshold and it is highly likely that the three-dimensional object is another vehicle, the process proceeds to step S46. In step S46, the second detection areas A12 and A22 whose widths are narrowed are expanded.
Specifically, when the moving speed of the three-dimensional object is other than a predetermined value, it can be determined that the three-dimensional object is not another vehicle. For this reason, in this embodiment, in order to give priority to improving the accuracy of detection of a three-dimensional object, the second detection areas A12 and A22 are arranged as cameras in the first detection areas A11 and A21 as shown in FIG. The area where the distance W in the lateral direction of the vehicle from theinstallation position 10 is equal to or greater than the predetermined distance W2, that is, the belt-shaped areas A13 and A23 outside the first detection area (the road shoulder side) is lost (narrowed in the vehicle width direction). ) Area.
On the other hand, when the moving speed of the three-dimensional object is within a predetermined value, it is considered that the three-dimensional object is likely to be another vehicle. For this reason, as shown in FIG. 29, the second detection areas A12 and A22 have a predetermined distance W2 + q (> W2) in the lateral direction W of the vehicle from the installation position of thecamera 10 in the first detection areas A11 and A21. The above region is a missing region. That is, the area to be deleted is reduced and the second detection areas A12 and A22 are expanded. Of course, the second detection region in this case may have the same area and shape as the first detection regions A11 and A21.
具体的に、立体物の移動速度が所定値以外である場合には、その立体物は他車両ではないと判断できる。このため、本実施形態では、立体物の検出の精度を向上させることを優先するために、第2検出領域A12,A22を、図22に示すように、第1検出領域A11,A21のうちカメラ10の設置位置から車両の側方方向の距離Wが所定距離W2以上の領域、つまり第1検出領域の外側(路肩側)の帯状の領域A13,A23を欠損させた(車幅方向に狭めた)領域とする。
他方、立体物の移動速度が所定値以内である場合には、その立体物が他車両である可能性が高いと考えられる。このため、第2検出領域A12,A22を、図29に示すように、第1検出領域A11,A21のうちカメラ10の設置位置から車両の側方方向の距離Wが所定距離W2+q(>W2)以上の領域を欠損させた領域とする。つまり欠損させる領域を小さくして、第2検出領域A12,A22を拡張する。もちろん、この場合の第2検出領域を、第1検出領域A11,A21と同じ面積及び形状としてもよい。 If it is determined in step S51 that the moving speed of the three-dimensional object calculated based on the moving distance is within the predetermined threshold and it is highly likely that the three-dimensional object is another vehicle, the process proceeds to step S46. In step S46, the second detection areas A12 and A22 whose widths are narrowed are expanded.
Specifically, when the moving speed of the three-dimensional object is other than a predetermined value, it can be determined that the three-dimensional object is not another vehicle. For this reason, in this embodiment, in order to give priority to improving the accuracy of detection of a three-dimensional object, the second detection areas A12 and A22 are arranged as cameras in the first detection areas A11 and A21 as shown in FIG. The area where the distance W in the lateral direction of the vehicle from the
On the other hand, when the moving speed of the three-dimensional object is within a predetermined value, it is considered that the three-dimensional object is likely to be another vehicle. For this reason, as shown in FIG. 29, the second detection areas A12 and A22 have a predetermined distance W2 + q (> W2) in the lateral direction W of the vehicle from the installation position of the
つぎに、ステップ42において、検出領域設定部34は、第1検出領域A11,A21と拡張された第2検出領域A12,A22とを設定する。立体物の移動速度が所定値域外である場合には、立体物が他車両である可能性が低いと考えられるので、植栽やガードレールを他車両として誤検出することを防止することを優先する。他方、立体物の移動速度が所定値域内である場合には、立体物が他車両である可能性が高いと考えられるので、検出範囲を最大として検出処理を行うことが好ましい。よって、本実施形態では、立体物が他車両である可能性が高い状態下においては、誤検出防止よりもすでに検出された他車両(立体物)の正確な検出結果を継続的に取得することを優先する。このように、本実施形態では、移動速度の観点から立体物が他車両である可能性が高い場合には、当初、路肩側の帯状の領域を欠損させた第2検出領域A12,A22を拡張するので、路肩建造物の影響により立体物が誤検出されることを防止しつつ、一度検出された他車両(立体物)を高い精度で監視することができる。
Next, in step 42, the detection area setting unit 34 sets the first detection areas A11, A21 and the extended second detection areas A12, A22. When the moving speed of the three-dimensional object is outside the predetermined value range, it is considered that the three-dimensional object is unlikely to be another vehicle, so priority is given to preventing erroneous detection of planting or guardrail as another vehicle. . On the other hand, when the moving speed of the three-dimensional object is within the predetermined range, it is considered that the three-dimensional object is likely to be another vehicle. Therefore, it is preferable to perform the detection process with the maximum detection range. Therefore, in the present embodiment, in a state where there is a high possibility that the three-dimensional object is another vehicle, the accurate detection result of the already detected other vehicle (three-dimensional object) is continuously acquired rather than prevention of erroneous detection. Priority. As described above, in the present embodiment, when there is a high possibility that the three-dimensional object is another vehicle from the viewpoint of moving speed, the second detection areas A12 and A22 in which the road-side belt-like area is initially lost are expanded. Therefore, the other vehicle (three-dimensional object) once detected can be monitored with high accuracy while preventing the three-dimensional object from being erroneously detected due to the influence of the roadside structure.
検出領域A1,A2が設定されたら、ステップS43に進み、立体物の検出処理を行う。この立体物の検出処理は上述した立体物検出部33による図11、図12の差分波形情報を用いた処理、又は立体物検出部37による図17、図18のエッジ情報を用いた処理に従って行われる。そして、ステップ43において、この立体物検出部33,37により検出領域A1,A2に立体物が検出された場合にはステップS45に進み、検出された立体物が他車両VXであると判断する。他方、立体物検出部33,37により検出領域A1,A2に立体物が検出されない場合にはステップS47に進み、検出領域A1,A2に他車両VXは存在しないと判断する。
If the detection areas A1 and A2 are set, the process proceeds to step S43 to perform a solid object detection process. This three-dimensional object detection processing is performed according to the above-described processing using the difference waveform information of FIGS. 11 and 12 by the three-dimensional object detection unit 33 or the processing using the edge information of FIGS. 17 and 18 by the three-dimensional object detection unit 37. Is called. In step 43, if a three-dimensional object is detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S45, and it is determined that the detected three-dimensional object is the other vehicle VX. On the other hand, when a solid object is not detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S47, and it is determined that no other vehicle VX exists in the detection areas A1 and A2.
(1)以上のとおり、本実施形態の立体物検出装置1によれば、立体物の移動距離を算出する場合には相対的に広い第1検出領域A11,A21を設定し、立体物の存在を検出する場合には相対的に狭い第2検出領域A12,A22を設定するので、自車両の走行車線の路肩の物体が検出領域に含まれることを防止するとともに、路肩に沿って所定周期で現れる物体の特徴が隣接車線の他車両VXの特徴として誤認される可能性を低くすることができる。この二つの第1検出領域A11,A21及び第2検出領域A12,A22を処理場面に応じて設定することにより、立体物の移動速度を正確に把握しつつ、道路の路肩の植栽、草、ガードレール、縁石などを他車両VXとして誤検出することなく、他車両VXを高い精度で検出することができる。
(1) As described above, according to the three-dimensional object detection device 1 of the present embodiment, when calculating the movement distance of the three-dimensional object, the relatively wide first detection areas A11 and A21 are set, and the presence of the three-dimensional object Since the second detection areas A12 and A22 that are relatively narrow are set when the vehicle is detected, it is possible to prevent an object on the road shoulder of the traveling lane of the host vehicle from being included in the detection area and at a predetermined cycle along the road shoulder. The possibility that the feature of the appearing object is mistaken as the feature of the other vehicle VX in the adjacent lane can be reduced. By setting these two first detection areas A11, A21 and second detection areas A12, A22 according to the processing scene, while accurately grasping the moving speed of the three-dimensional object, planting the road shoulder, grass, The other vehicle VX can be detected with high accuracy without erroneously detecting a guardrail, a curbstone, or the like as the other vehicle VX.
(2)本実施形態の立体物検出装置1によれば、第2検出領域A12を、第1検出領域11のうちカメラ10からの後方距離が所定距離以上の領域A13を欠損させた領域とすることにより、路肩の植栽、草、ガードレール、縁石などの映像から周期的な特徴を抽出しにくくして、同一の物体のような特徴点が画像情報に含まれてしまう確率、及び画像情報に含まれる特徴点が他車両VXの特徴点と似てしまう確率を低下させることができる。これにより、自車両の走行車線の路肩の物体を自車両の走行車線の隣の隣接車線を走行する他車両として誤検出することを防止することができるので、自車両の走行車線の隣の隣接車線を走行する他車両を、高い精度で検出することができる。
(2) According to the three-dimensional object detection device 1 of the present embodiment, the second detection region A12 is a region in which the region A13 whose back distance from the camera 10 is greater than or equal to a predetermined distance in the first detection region 11 is lost. This makes it difficult to extract periodic features from images of roadside planting, grass, guardrails, curbs, etc., and the probability that feature points like the same object will be included in the image information, and image information It is possible to reduce the probability that the feature points included are similar to the feature points of the other vehicle VX. As a result, it is possible to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle. Other vehicles traveling in the lane can be detected with high accuracy.
(3)本実施形態の立体物検出装置1によれば、第2検出領域A12を、第1検出領域11のうちカメラ10からの側方距離が所定距離以上の領域A13を欠損させた領域とすることにより、路肩の植栽、草、ガードレール、縁石などの映像が検出処理に用いられる画像情報に含まれないようにすることができるので、そもそも、路肩の植栽、草、ガードレール、縁石などについて立体物であるか否かの判断をしないようにすることができる。これにより、自車両の走行車線の路肩の物体を自車両の走行車線の隣の隣接車線を走行する他車両として誤検出することを防止することができるので、自車両の走行車線の隣の隣接車線を走行する他車両を、高い精度で検出することができる。
(3) According to the three-dimensional object detection device 1 of the present embodiment, the second detection area A12 is defined as an area in which the area A13 whose lateral distance from the camera 10 is greater than or equal to a predetermined distance in the first detection area 11 is lost. By doing so, images of roadside planting, grass, guardrail, curb, etc. can be excluded from the image information used for detection processing, so in the first place, roadside planting, grass, guardrail, curb, etc. It is possible not to determine whether or not it is a three-dimensional object. As a result, it is possible to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle. Other vehicles traveling in the lane can be detected with high accuracy.
(4)本実施形態の立体物検出装置1によれば、第2検出領域A12,A22は、第1検出領域A11,A21のうち車両前方側の右端に位置する頂点を含む領域E1を欠損させた領域であり、第2検出領域A12,A22は、第1検出領域A11.A21のうち車両前方側の左端に位置する頂点E2を含む領域を欠損させた領域であるので、上述の(2)(3)と同様の作用を奏し、立体物の移動速度を正確に把握しつつも、道路の路肩の植栽、草、ガードレール、縁石などを他車両VXとして誤検出することなく、他車両VXを高い精度で検出することができる。
(4) According to the three-dimensional object detection device 1 of the present embodiment, the second detection areas A12 and A22 are missing the area E1 including the vertex located at the right end on the vehicle front side of the first detection areas A11 and A21. The second detection areas A12, A22 are the first detection areas A11. Since the region including the vertex E2 located at the left end of the vehicle front side in A21 is missing, the same effect as the above (2) and (3) is obtained, and the moving speed of the three-dimensional object is accurately grasped. However, it is possible to detect the other vehicle VX with high accuracy without erroneously detecting planting of a road shoulder, grass, guardrail, curbstone, and the like as the other vehicle VX.
(5)なお、立体物の移動距離を算出する場合に検出領域設定部34により設定される第1検出領域は、同じく検出領域設定部34により車両後方の右側及び左側のそれぞれに設定される、例えばデフォルトに定義された検出領域と、同じ大きさとすることにより、演算負荷を低減させて、安定した検出結果を導出させることができる。
(5) Note that the first detection area set by the detection area setting unit 34 when calculating the movement distance of the three-dimensional object is similarly set by the detection area setting unit 34 on each of the right and left sides behind the vehicle. For example, by making the detection area the same size as the default detection area, the calculation load can be reduced and a stable detection result can be derived.
(6)本実施形態の立体物検出装置1によれば、立体物が接近している場合には、拡張された第2検出領域A12,A22を設定するので、立体物の検出を慎重に行うことができ、他車両VXの検出の精度を高めることができる。
(6) According to the three-dimensional object detection device 1 of the present embodiment, when the three-dimensional object is approaching, the expanded second detection areas A12 and A22 are set, so that the three-dimensional object is carefully detected. And the accuracy of detection of the other vehicle VX can be increased.
(7)なお、本実施形態の立体物の検出方法においても同様の作用及び同様の効果を得ることができる。
(7) It should be noted that the same action and the same effect can be obtained also in the method for detecting a three-dimensional object of the present embodiment.
(8)本実施形態の立体物検出装置1によれば、立体物の検出処理は路肩側の領域を欠損させた第2検出領域の画像情報に基づいて行い、立体物の移動距離(移動速度)の算出処理は欠損の無い第1検出領域の画像情報に基づいて行うことを前提とし、立体物の移動速度に基づき、その立体物が他車両である可能性が高いと判断した場合には、第2検出領域の欠損部分を減らす、つまり第2検出領域を路肩側へ拡張する。このため、通常は、検出領域の路肩側を欠損させて路肩側の構造物の影響による誤検出を抑制しつつ、他車両が存在する可能性が高い場合には、広い検出領域で立体物(他車両)を検出することができる。しかも、立体物の移動速度は広い検出領域の画像情報に基づいて行うので、移動速度の算出結果の精度を担保することができる。
(8) According to the three-dimensional object detection device 1 of the present embodiment, the three-dimensional object detection process is performed based on the image information of the second detection region in which the roadside region is missing, and the movement distance (movement speed of the three-dimensional object) ) Is calculated based on the image information of the first detection area having no defect, and based on the moving speed of the three-dimensional object, it is determined that the three-dimensional object is likely to be another vehicle. , The missing part of the second detection area is reduced, that is, the second detection area is expanded to the roadside. For this reason, in general, when there is a high possibility that another vehicle exists while the road shoulder side of the detection area is lost to suppress erroneous detection due to the influence of the structure on the road shoulder side, a three-dimensional object ( Other vehicle) can be detected. Moreover, since the moving speed of the three-dimensional object is performed based on image information of a wide detection area, the accuracy of the calculation result of the moving speed can be ensured.
上記カメラ10は本発明に係る撮像手段に相当し、上記視点変換部31は本発明に係る画像変換手段に相当し、上記位置合わせ部32及び立体物検出部33は本発明に係る立体物検出手段に相当し、上記輝度差算出部35,エッジ線検出部36及び立体物検出部37は本発明に係る立体物検出手段に相当し、上記検出領域設定部34は検出領域設定手段に相当し、上記車速センサ20は車速センサに相当する。
The camera 10 corresponds to an imaging unit according to the present invention, the viewpoint conversion unit 31 corresponds to an image conversion unit according to the present invention, and the alignment unit 32 and the three-dimensional object detection unit 33 include a three-dimensional object detection according to the present invention. The luminance difference calculation unit 35, the edge line detection unit 36, and the three-dimensional object detection unit 37 correspond to a three-dimensional object detection unit according to the present invention, and the detection region setting unit 34 corresponds to a detection region setting unit. The vehicle speed sensor 20 corresponds to a vehicle speed sensor.
1…立体物検出装置
10…カメラ
20…車速センサ
30…計算機
31…視点変換部
32…位置合わせ部
33,37…立体物検出部
34…検出領域設定部
35…輝度差算出部
36…エッジ検出部
50…操舵角センサ
40…スミア検出部
a…画角
A1,A2…検出領域
CP…交点
DP…差分画素
DWt,DWt’…差分波形
DWt1~DWm,DWm+k~DWtn…小領域
L1,L2…接地線
La,Lb…立体物が倒れ込む方向上の線
P…撮像画像
PBt…鳥瞰視画像
PDt…差分画像
MP…マスク画像
S…スミア
SP…スミア画像
SBt…スミア鳥瞰視画像
V…自車両
VX…他車両 DESCRIPTION OFSYMBOLS 1 ... Three-dimensional object detection apparatus 10 ... Camera 20 ... Vehicle speed sensor 30 ... Computer 31 ... Viewpoint conversion part 32 ... Position alignment part 33, 37 ... Three-dimensional object detection part 34 ... Detection area setting part 35 ... Luminance difference calculation part 36 ... Edge detection Section 50 ... Steering angle sensor 40 ... Smear detection section a ... Angle of view A1, A2 ... Detection area CP ... Intersection DP ... Difference pixels DW t , DW t '... Difference waveforms DW t1 to DW m , DW m + k to DW tn ... Small regions L1, L2 ... ground line La, Lb ... line P on the direction in which the three-dimensional object collapses ... captured image PB t ... bird's-eye view image PD t ... difference image MP ... mask image S ... smear SP ... smear image SB t ... smear bird's Visual image V ... own vehicle VX ... other vehicle
10…カメラ
20…車速センサ
30…計算機
31…視点変換部
32…位置合わせ部
33,37…立体物検出部
34…検出領域設定部
35…輝度差算出部
36…エッジ検出部
50…操舵角センサ
40…スミア検出部
a…画角
A1,A2…検出領域
CP…交点
DP…差分画素
DWt,DWt’…差分波形
DWt1~DWm,DWm+k~DWtn…小領域
L1,L2…接地線
La,Lb…立体物が倒れ込む方向上の線
P…撮像画像
PBt…鳥瞰視画像
PDt…差分画像
MP…マスク画像
S…スミア
SP…スミア画像
SBt…スミア鳥瞰視画像
V…自車両
VX…他車両 DESCRIPTION OF
Claims (15)
- 車両に搭載され、車両後方を撮像する撮像手段と、
前記車両後方の右側及び左側のそれぞれに検出領域を設定する検出領域設定手段と、
前記撮像手段により得られた画像を鳥瞰視画像に視点変換する画像変換手段と、
前記画像変換手段により得られた異なる時刻の鳥瞰視画像の位置を鳥瞰視上で位置合わせし、当該位置合わせされた鳥瞰視画像の差分画像上において、前記鳥瞰視画像を視点変換した際に立体物が倒れ込む方向に沿って、前記差分画像上において所定の差分を示す画素数をカウントして度数分布化することで差分波形情報を生成し、当該差分波形情報に基づいて、前記検出領域に存在する立体物を検出するとともに、前記差分波形情報の波形の時間変化から立体物の移動距離を算出する立体物検出手段と、を備え、
前記検出領域設定手段は、前記立体物検出手段が立体物の移動距離を算出する場合には第1検出領域を設定し、前記立体物検出手段が前記検出領域に存在する立体物を検出する場合には前記第1検出領域よりも面積が小さい第2検出領域を設定することを特徴とする立体物検出装置。 An imaging means mounted on the vehicle for imaging the rear of the vehicle;
Detection area setting means for setting a detection area on each of the right and left sides behind the vehicle;
Image conversion means for converting the image obtained by the imaging means into a bird's eye view image;
The positions of the bird's-eye view images at different times obtained by the image conversion means are aligned on the bird's-eye view, and a three-dimensional view is obtained when the bird's-eye view image is subjected to viewpoint conversion on the difference image of the aligned bird's-eye view images. The difference waveform information is generated by counting the number of pixels indicating a predetermined difference on the difference image and performing frequency distribution along the direction in which the object falls, and exists in the detection region based on the difference waveform information. A three-dimensional object detecting means for detecting a three-dimensional object to be calculated and calculating a moving distance of the three-dimensional object from a time change of the waveform of the differential waveform information,
The detection area setting means sets a first detection area when the three-dimensional object detection means calculates a moving distance of the three-dimensional object, and the three-dimensional object detection means detects a three-dimensional object existing in the detection area. The three-dimensional object detection apparatus is characterized in that a second detection region having a smaller area than the first detection region is set in the first detection region. - 車両に搭載され、車両後方を撮像する撮像手段と、
前記車両後方の右側及び左側のそれぞれに検出領域を設定する検出領域設定手段と、
前記撮像手段により得られた画像を鳥瞰視画像に視点変換する画像変換手段と、
前記画像変換手段により得られた鳥瞰視画像において、鳥瞰視画像に視点変換した際に立体物が倒れ込む方向に沿って、互いに隣接する画像領域の輝度差が所定閾値以上であるエッジ情報を検出し、当該エッジ情報に基づいて立体物を検出するとともに、前記エッジ情報の時間変化から立体物の移動距離を算出する立体物検出手段と、を備え、
前記検出領域設定手段は、前記立体物検出手段が立体物の移動距離を算出する場合には第1検出領域を設定し、前記立体物検出手段が前記検出領域に存在する立体物を検出する場合には前記第1検出領域よりも面積が小さい第2検出領域を設定することを特徴とする立体物検出装置。 An imaging means mounted on the vehicle for imaging the rear of the vehicle;
Detection area setting means for setting a detection area on each of the right and left sides behind the vehicle;
Image conversion means for converting the image obtained by the imaging means into a bird's eye view image;
In the bird's-eye view image obtained by the image conversion means, edge information whose luminance difference between adjacent image areas is equal to or greater than a predetermined threshold is detected along the direction in which the three-dimensional object falls when the viewpoint is converted into the bird's-eye view image. And a solid object detecting means for detecting a solid object based on the edge information and calculating a moving distance of the solid object from a time change of the edge information,
The detection area setting means sets a first detection area when the three-dimensional object detection means calculates a moving distance of the three-dimensional object, and the three-dimensional object detection means detects a three-dimensional object existing in the detection area. The three-dimensional object detection apparatus is characterized in that a second detection region having a smaller area than the first detection region is set in the first detection region. - 前記第2検出領域は、前記第1検出領域のうち前記カメラの設置位置を基準とした後方距離が所定距離以上の領域を欠損させた領域であることを特徴とする請求項1又は2に記載の立体物検出装置。 The said 2nd detection area is an area | region which lost | deleted the area | region where the back distance on the basis of the installation position of the said camera is a predetermined distance or more among the said 1st detection areas. Three-dimensional object detection device.
- 前記第2検出領域は、前記第1検出領域のうち前記カメラの設置位置を基準とした側方距離が所定距離以上の領域を欠損させた領域であることを特徴とする請求項1又は2に記載の立体物検出装置。 The said 2nd detection area is an area | region which lost the area | region where the side distance on the basis of the installation position of the said camera is a predetermined distance or more among the said 1st detection areas. The three-dimensional object detection apparatus described.
- 前記カメラの設置位置を基準とした側方距離の所定距離は、前記カメラから離隔するに従い大きくなることを特徴とする請求項4に記載の立体物検出装置。 5. The three-dimensional object detection device according to claim 4, wherein a predetermined lateral distance based on the installation position of the camera increases as the distance from the camera increases.
- 前記第2検出領域は、前記第1検出領域のうち前記カメラの設置位置を基準とした後方距離が所定距離以内かつ前記カメラの設置位置を基準とした側方距離が所定距離以上である領域を欠損させた領域であることを特徴とする請求項1又は2に記載の立体物検出装置。 The second detection area is an area of the first detection area in which a rear distance based on the installation position of the camera is within a predetermined distance and a lateral distance based on the installation position of the camera is a predetermined distance or more. The three-dimensional object detection device according to claim 1, wherein the three-dimensional object detection device is a deficient region.
- 前記車両後方の右側に設定された前記第2検出領域は、前記車両後方の右側に設定された前記第1検出領域のうち車両前方側の右端に位置する頂点を含む領域を欠損させた領域であり、前記車両後方の左側に設定された前記第2検出領域は、前記車両後方の左側に設定された前記第1検出領域のうち車両前方側の左端に位置する頂点を含む領域を欠損させた領域であることを特徴とする請求項1又は2に記載の立体物検出装置。 The second detection region set on the right side behind the vehicle is a region in which the region including the vertex located at the right end on the vehicle front side is missing from the first detection region set on the right side behind the vehicle. Yes, the second detection area set on the left side behind the vehicle is missing the area including the apex located at the left end on the vehicle front side among the first detection areas set on the left side behind the vehicle The three-dimensional object detection device according to claim 1, wherein the three-dimensional object detection device is a region.
- 車両に搭載され、車両後方を撮像する撮像手段と、
前記車両後方の右側及び左側のそれぞれに検出領域を設定する検出領域設定手段と、
前記撮像手段により得られた画像を鳥瞰視画像に視点変換する画像変換手段と、
前記画像変換手段により得られた異なる時刻の鳥瞰視画像の位置を鳥瞰視上で位置合わせし、当該位置合わせされた鳥瞰視画像の差分画像上において、前記鳥瞰視画像を視点変換した際に立体物が倒れ込む方向に沿って、前記差分画像上において所定の差分を示す画素数をカウントして度数分布化することで差分波形情報を生成し、当該差分波形情報に基づいて、前記検出領域に存在する立体物を検出するとともに、前記差分波形情報の波形の時間変化から立体物の移動距離を算出する立体物検出手段と、を備え、
前記検出領域設定手段は、前記立体物検出手段が立体物の移動距離を算出する場合には第1検出領域を設定し、前記立体物検出手段が前記検出領域に存在する立体物を検出する場合には前記第1検出領域とは異なる第2検出領域を設定し、
前記車両後方の右側に設定された前記第2検出領域は、前記車両後方の右側に設定された前記第1検出領域のうち車両前方側の右端に位置する頂点を含む領域を欠損させた領域であり、前記車両後方の左側に設定された前記第2検出領域は、前記車両後方の左側に設定された前記第1検出領域のうち車両前方側の左端に位置する頂点を含む領域を欠損させた領域であることを特徴とする立体物検出装置。 An imaging means mounted on the vehicle for imaging the rear of the vehicle;
Detection area setting means for setting a detection area on each of the right and left sides behind the vehicle;
Image conversion means for converting the image obtained by the imaging means into a bird's eye view image;
The positions of the bird's-eye view images at different times obtained by the image conversion means are aligned on the bird's-eye view, and a three-dimensional view is obtained when the bird's-eye view image is subjected to viewpoint conversion on the difference image of the aligned bird's-eye view images. The difference waveform information is generated by counting the number of pixels indicating a predetermined difference on the difference image and performing frequency distribution along the direction in which the object falls, and exists in the detection region based on the difference waveform information. A three-dimensional object detecting means for detecting a three-dimensional object to be calculated and calculating a moving distance of the three-dimensional object from a time change of the waveform of the differential waveform information,
The detection area setting means sets a first detection area when the three-dimensional object detection means calculates a moving distance of the three-dimensional object, and the three-dimensional object detection means detects a three-dimensional object existing in the detection area. Set a second detection area different from the first detection area,
The second detection region set on the right side behind the vehicle is a region in which the region including the apex located at the right end on the vehicle front side is missing from the first detection region set on the right side behind the vehicle. Yes, the second detection area set on the left side behind the vehicle lacks the area including the apex located at the left end on the vehicle front side in the first detection area set on the left side behind the vehicle A three-dimensional object detection device characterized by being an area. - 車両に搭載され、車両後方を撮像する撮像手段と、
前記車両後方の右側及び左側のそれぞれに検出領域を設定する検出領域設定手段と、
前記撮像手段により得られた画像を鳥瞰視画像に視点変換する画像変換手段と、
前記画像変換手段により得られた鳥瞰視画像において、鳥瞰視画像に視点変換した際に立体物が倒れ込む方向に沿って、互いに隣接する画像領域の輝度差が所定閾値以上であるエッジ情報を検出し、当該エッジ情報に基づいて立体物を検出するとともに、前記エッジ情報の時間変化から立体物の移動距離を算出する立体物検出手段と、を備え、
前記検出領域設定手段は、前記立体物検出手段が立体物の移動距離を算出する場合には第1検出領域を設定し、前記立体物検出手段が前記検出領域に存在する立体物を検出する場合には前記第1検出領域とは異なる第2検出領域を設定し、
前記車両後方の右側に設定された前記第2検出領域は、前記車両後方の右側に設定された前記第1検出領域のうち車両前方側の右端に位置する頂点を含む領域を欠損させた領域であり、前記車両後方の左側に設定された前記第2検出領域は、前記車両後方の左側に設定された前記第1検出領域のうち車両前方側の左端に位置する頂点を含む領域を欠損させた領域であることを特徴とする立体物検出装置。 An imaging means mounted on the vehicle for imaging the rear of the vehicle;
Detection area setting means for setting a detection area on each of the right and left sides behind the vehicle;
Image conversion means for converting the image obtained by the imaging means into a bird's eye view image;
In the bird's-eye view image obtained by the image conversion means, edge information whose luminance difference between adjacent image areas is equal to or greater than a predetermined threshold is detected along the direction in which the three-dimensional object falls when the viewpoint is converted into the bird's-eye view image. And a solid object detecting means for detecting a solid object based on the edge information and calculating a moving distance of the solid object from a time change of the edge information,
The detection area setting means sets a first detection area when the three-dimensional object detection means calculates a moving distance of the three-dimensional object, and the three-dimensional object detection means detects a three-dimensional object existing in the detection area. Set a second detection area different from the first detection area,
The second detection region set on the right side behind the vehicle is a region in which the region including the apex located at the right end on the vehicle front side is missing from the first detection region set on the right side behind the vehicle. Yes, the second detection area set on the left side behind the vehicle lacks the area including the apex located at the left end on the vehicle front side in the first detection area set on the left side behind the vehicle A three-dimensional object detection device characterized by being an area. - 前記検出領域設定手段は、前記立体物検出手段が前記自車両に接近する立体物を検出した場合には、前記第2検出領域を拡張することを特徴とする請求項1~9の何れか一項に記載の立体物検出装置。 10. The detection area setting means, when the solid object detection means detects a solid object approaching the host vehicle, the detection area setting means extends the second detection area. The three-dimensional object detection device according to item.
- 前記立体物検出手段が立体物の移動距離を算出する場合に前記検出領域設定手段により設定される第1検出領域は、前記検出領域設定手段により前記車両後方の右側及び左側のそれぞれに設定される検出領域と、同じ大きさであることを特徴とする請求項1~10の何れか一項に記載の立体物検出装置。 The first detection area set by the detection area setting means when the three-dimensional object detection means calculates the movement distance of the three-dimensional object is set on each of the right and left sides behind the vehicle by the detection area setting means. The three-dimensional object detection device according to any one of claims 1 to 10, wherein the three-dimensional object detection device has the same size as the detection region.
- 車両に搭載されたカメラにより撮像された車両後方の画像情報を取得するステップと、
前記車両後方の右側及び左側のそれぞれに検出領域を設定するステップと、
前記画像情報に基づいて前記検出領域に存在する立体物を検出するとともに、前記立体物の移動距離を算出するステップと、
前記検出領域を設定するステップにおいて、前記立体物の移動距離を算出する場合には第1検出領域を設定し、前記検出領域に存在する立体物を検出する場合には第1検出領域よりも面積が小さい第2検出領域を設定することを特徴とする立体物検出方法。 Obtaining image information behind the vehicle imaged by a camera mounted on the vehicle;
Setting a detection area on each of the right and left sides behind the vehicle;
Detecting a solid object existing in the detection region based on the image information, and calculating a moving distance of the solid object;
In the step of setting the detection area, the first detection area is set when calculating the moving distance of the three-dimensional object, and the area is larger than the first detection area when detecting the three-dimensional object existing in the detection area. A three-dimensional object detection method characterized by setting a second detection region having a small size. - 車両に搭載されたカメラにより撮像された車両後方の画像情報を取得するステップと、
前記車両後方の右側及び左側のそれぞれに検出領域を設定するステップと、
前記画像情報に基づいて前記検出領域に存在する立体物を検出するとともに、前記立体物の移動距離を算出するステップと、
前記検出領域を設定するステップにおいて、前記立体物の移動距離を算出する場合には第1検出領域を設定し、前記検出領域に存在する立体物を検出する場合には第1検出領域とは異なる第2検出領域を設定する立体物検出方法であって、
前記車両後方の右側に設定される前記第2検出領域は、前記車両後方の右側に設定された前記第1検出領域のうち車両前方側の右端に位置する頂点を含む領域を欠損させた領域であり、前記車両後方の左側に設定される前記第2検出領域は、前記車両後方の左側に設定された前記第1検出領域のうち車両前方側の左端に位置する頂点を含む領域を欠損させた領域であることを特徴とする立体物検出方法。 Obtaining image information behind the vehicle imaged by a camera mounted on the vehicle;
Setting a detection area on each of the right and left sides behind the vehicle;
Detecting a solid object existing in the detection region based on the image information, and calculating a moving distance of the solid object;
In the step of setting the detection area, a first detection area is set when calculating a moving distance of the three-dimensional object, and different from the first detection area when detecting a three-dimensional object existing in the detection area. A solid object detection method for setting a second detection region,
The second detection region set on the right side behind the vehicle is a region in which a region including a vertex located at the right end on the vehicle front side is missing from the first detection region set on the right side behind the vehicle. Yes, the second detection region set on the left side behind the vehicle is missing the region including the apex located at the left end on the vehicle front side in the first detection region set on the left side behind the vehicle A three-dimensional object detection method characterized by being an area. - 車両に搭載され、車両後方を撮像する撮像手段と、
前記車両後方の右側及び左側のそれぞれに、第1の検出領域と当該第1検出領域のうち前記撮像手段の設置位置から前記車両の側方方向の距離が所定距離以上の領域を欠損させた第2検出領域とを含む検出領域を設定する検出領域設定手段と、
前記撮像手段により得られた画像を鳥瞰視画像に視点変換する画像変換手段と、
前記画像変換手段により得られた異なる時刻の鳥瞰視画像の位置を鳥瞰視上で位置合わせし、当該位置合わせされた鳥瞰視画像の差分画像上において、前記鳥瞰視画像を視点変換した際に立体物が倒れ込む方向に沿って、前記差分画像上において所定の差分を示す画素数をカウントして度数分布化することで差分波形情報を生成し、当該差分波形情報に基づいて、前記検出領域に存在する立体物を検出するとともに、前記差分波形情報の波形の時間変化から立体物の移動距離を算出する立体物検出手段と、を備え、
前記立体物検出手段は、前記検出領域設定手段により設定された第1検出領域の画像情報に基づいて前記立体物の移動距離を算出するとともに、前記検出領域設定手段により設定された前記第2検出領域の画像情報に基づいて前記立体物を検出し、
前記検出領域設定手段は、前記移動距離に基づく前記立体物の移動速度が所定値域内である場合には、前記第2検出領域を前記撮像手段の設置位置から前記車両の側方方向へ拡張することを特徴とする立体物検出装置。 An imaging means mounted on the vehicle for imaging the rear of the vehicle;
In each of the right and left sides behind the vehicle, a first detection region and a region of the first detection region where the distance in the lateral direction of the vehicle from the installation position of the imaging means is a predetermined distance or more are lost. Detection area setting means for setting a detection area including two detection areas;
Image conversion means for converting the image obtained by the imaging means into a bird's eye view image;
The positions of the bird's-eye view images at different times obtained by the image conversion means are aligned on the bird's-eye view, and a three-dimensional view is obtained when the bird's-eye view image is subjected to viewpoint conversion on the difference image of the aligned bird's-eye view images. The difference waveform information is generated by counting the number of pixels indicating a predetermined difference on the difference image and performing frequency distribution along the direction in which the object falls, and exists in the detection region based on the difference waveform information. A three-dimensional object detecting means for detecting a three-dimensional object to be calculated and calculating a moving distance of the three-dimensional object from a time change of the waveform of the differential waveform information,
The three-dimensional object detection means calculates the movement distance of the three-dimensional object based on the image information of the first detection area set by the detection area setting means, and the second detection set by the detection area setting means. Detecting the three-dimensional object based on the image information of the region;
The detection area setting means extends the second detection area from the installation position of the imaging means in the lateral direction of the vehicle when the moving speed of the three-dimensional object based on the moving distance is within a predetermined value range. The three-dimensional object detection apparatus characterized by the above-mentioned. - 車両に搭載され、車両後方を撮像する撮像手段と、
前記車両後方の右側及び左側のそれぞれに、第1の検出領域と当該第1検出領域のうち前記撮像手段の設置位置から前記車両の側方方向の距離が所定距離以上の領域を欠損させた第2検出領域とを含む検出領域を設定する検出領域設定手段と、
前記撮像手段により得られた画像を鳥瞰視画像に視点変換する画像変換手段と、
前記画像変換手段により得られた鳥瞰視画像において、鳥瞰視画像に視点変換した際に立体物が倒れ込む方向に沿って、互いに隣接する画像領域の輝度差が所定閾値以上であるエッジ情報を検出し、当該エッジ情報に基づいて立体物を検出するとともに、前記エッジ情報の時間変化から立体物の移動距離を算出する立体物検出手段と、を備え、
前記立体物検出手段は、前記検出領域設定手段により設定された第1検出領域の画像情報に基づいて前記立体物の移動距離を算出するとともに、前記検出領域設定手段により設定された前記第2検出領域の画像情報に基づいて前記立体物を検出し、
前記検出領域設定手段は、前記移動距離に基づく前記立体物の移動速度が所定値域内である場合には、前記第2検出領域を前記撮像手段の設置位置から前記車両の側方方向へ拡張することを特徴とする立体物検出装置。 An imaging means mounted on the vehicle for imaging the rear of the vehicle;
In each of the right and left sides behind the vehicle, a first detection region and a region of the first detection region where the distance in the lateral direction of the vehicle from the installation position of the imaging means is a predetermined distance or more are lost. Detection area setting means for setting a detection area including two detection areas;
Image conversion means for converting the image obtained by the imaging means into a bird's eye view image;
In the bird's-eye view image obtained by the image conversion means, edge information whose luminance difference between adjacent image areas is equal to or greater than a predetermined threshold is detected along the direction in which the three-dimensional object falls when the viewpoint is converted into the bird's-eye view image. And a solid object detecting means for detecting a solid object based on the edge information and calculating a moving distance of the solid object from a time change of the edge information,
The three-dimensional object detection means calculates the movement distance of the three-dimensional object based on the image information of the first detection area set by the detection area setting means, and the second detection set by the detection area setting means. Detecting the three-dimensional object based on the image information of the region;
The detection area setting means extends the second detection area from the installation position of the imaging means in the lateral direction of the vehicle when the moving speed of the three-dimensional object based on the moving distance is within a predetermined value range. The three-dimensional object detection apparatus characterized by the above-mentioned.
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