JP4358183B2 - Vehicle periphery monitoring device - Google Patents

Description

  The present invention relates to a vehicle periphery monitoring apparatus that extracts an object by binarization processing of an image photographed by an infrared camera.

Conventionally, an object such as a pedestrian who may collide with the own vehicle is extracted from an infrared image around the own vehicle obtained by photographing with an infrared camera, and information on the object is transmitted to the driver of the own vehicle. A display processing device to be provided is known (for example, see Patent Document 1).
In this display processing device, binarization processing is performed on the infrared image to search for a region where the bright portion is concentrated (binarization target object), and the aspect ratio and sufficiency ratio of the binarization target object, as well as the actual area And the distance calculated by the center of gravity position on the infrared image, it is determined whether or not the binarized object is a pedestrian's head. Then, from the distance of the pedestrian's head region from the infrared camera and the average height of the adult, the height of the pedestrian on the infrared image is calculated, and a body region including the pedestrian's body is set, By displaying the body region and the body region separately from other regions, the driver's visual assistance for the pedestrian is performed.
JP 11-328364 A

By the way, in the display processing device according to an example of the above prior art, since the pedestrian is detected based on the shape determination for the head region and the body region on the infrared image, the shape of the pedestrian, particularly the head There is a risk that it may be difficult to distinguish a pedestrian from an artificial structure having a size and height similar to the shape of the heat generating and generating heat.
The present invention has been made in view of the above circumstances, and an object thereof is to provide a vehicle periphery monitoring device capable of accurately distinguishing and extracting a pedestrian and an artificial structure on an infrared image.

In order to solve the above problems and achieve the object, the vehicle periphery monitoring device according to the present invention is obtained by imaging by infrared imaging means (for example, the infrared cameras 2R and 2L in the embodiment). A vehicle periphery monitoring device that extracts an object existing outside the host vehicle as an object based on the obtained image, and binarized object from image data obtained by binarizing the grayscale image of the image A binarized object extracting unit (eg, step S7 in the embodiment) for extracting the binarized object and the binarized object extracted by the binarized object extracting unit on the grayscale image Area setting means (for example, step S34 in the embodiment) for setting an area (for example, mask OA in the embodiment), and a vertical direction that is a vertical direction in the area set by the area setting means each Average luminance value in the horizontal direction at each position (e.g., horizontal average luminance projection F (J) in the embodiment) lateral average luminance value calculating means for calculating a (e.g., serving also as the step S34 in the embodiment) Determination means for determining whether or not a change according to the position in the vertical direction of the average luminance value calculated by the horizontal average luminance value calculating means is a predetermined change (for example, step S34 in the embodiment includes And object type determination means for determining the type of the object based on the determination result of the determination means (for example, also serves as step S34 in the embodiment), and the predetermined change has a plurality of maximums. position (e.g., maximum position in the embodiment Hi 1, Hi2) and minimum positions (e.g., maximum position Low1, low2 in the embodiment) has, the said maximum position adjacent poles Luminance difference value between the position is characterized by a predetermined value or more.

  According to the vehicle periphery monitoring device described above, the luminance distribution determination unit is configured to calculate the luminance state amount calculated by the luminance state amount calculation unit, for example, the luminance value for each pixel or the distribution of the average value of luminance values along a predetermined direction. Based on this, it is determined whether or not a predetermined luminance distribution, such as a distribution shape or the number of distributions, exists in the region set by the region setting means. Here, as the predetermined luminance distribution, for example, by setting the luminance distribution based on the structural features of the predetermined artificial structure, it is possible to discriminate between the artificial structure and other than the artificial structure as the type of the object. .

  According to the vehicle periphery monitoring device described above, for example, it is possible to prevent a luminance distribution including a plurality of maximum areas having substantially the same maximum luminance value from being erroneously recognized as the head of a pedestrian.

Furthermore, the vehicle periphery monitoring device according to the present invention described in claim 2 includes pedestrian recognition means (for example, step S35 in the embodiment) for recognizing a pedestrian existing in the outside of the host vehicle based on the image. , pedestrian recognition means is characterized in that performing the pedestrian recognition processing on the given the object determined to be other than artificial structure by the object type determination means.

  According to the vehicle periphery monitoring device, in addition to the object determined to be a pedestrian, walking is performed by performing a pedestrian recognition process on the object determined to be other than an artificial structure. The recognition accuracy of the person can be improved.

Moreover, the vehicle periphery monitoring device of the present invention described in claim 3, an alarm output means for outputting an alarm to the predetermined the object determined to be other than artificial structure by the object type determination means (For example, step S19 in the embodiment).

According to the above vehicle periphery monitoring device, in addition to the object determined to be a pedestrian, an alarm is output for an object determined to be other than an artificial structure. It is possible to prevent unnecessary alarms from being output .

As described above, according to the vehicle periphery monitoring device of the present invention described in claim 1, as the predetermined luminance distribution, for example, by setting the luminance distribution based on the structural features of the predetermined artificial structure, As an object type, it is possible to accurately discriminate between an artificial structure and a non-artificial structure.
Furthermore , for example, it is possible to prevent a luminance distribution including a plurality of maximum areas having substantially the same maximum luminance value from being erroneously recognized as the head of a pedestrian.
Furthermore, according to the vehicle periphery monitoring apparatus of the present invention as set forth in claim 2 , the recognition accuracy of a pedestrian can be improved.
Furthermore, according to the vehicle periphery monitoring device of the present invention described in claim 3 , it is possible to prevent an unnecessary alarm from being output to the artificial structure.
Furthermore, according to the vehicle periphery monitoring apparatus of the present invention as set forth in claims 4 to 6, the object is an artificial structure in which two heating elements are arranged at predetermined intervals along the vertical direction. Can be determined.

Hereinafter, a vehicle periphery monitoring device according to an embodiment of the present invention will be described with reference to the accompanying drawings.
As shown in FIG. 1, for example, the vehicle periphery monitoring device according to the present embodiment includes an image processing unit 1 including a CPU (central processing unit) that controls the vehicle periphery monitoring device, and two infrared rays that can detect far infrared rays. Cameras 2R, 2L, a yaw rate sensor 3 for detecting the yaw rate of the host vehicle, a vehicle speed sensor 4 for detecting the traveling speed (vehicle speed) of the host vehicle, a brake sensor 5 for detecting presence or absence of a brake operation by the driver, and a speaker 6 and a display device 7, for example, the image processing unit 1 is detected by an infrared image around the host vehicle obtained by photographing with two infrared cameras 2R and 2L, and sensors 3, 4 and 5. It is possible to detect a moving body such as a pedestrian or an animal ahead of the traveling direction of the host vehicle from a detection signal related to the traveling state of the host vehicle, and contact between the detected moving body and the host vehicle may occur. When it is determined that there is sex, and outputs an alarm via a loudspeaker 6 or the display device 7.
For example, the display device 7 is a display device integrated with instruments that display various travel state quantities of the host vehicle, a display device of a navigation device, and various information at a position that does not obstruct the driver's front view in the front window. HUD (Head Up Display) 7a and the like are displayed.

  The image processing unit 1 includes an A / D conversion circuit that converts an input analog signal into a digital signal, an image memory that stores a digitized image signal, a CPU (central processing unit) that performs various arithmetic processing, and a CPU RAM (Random Access Memory) used to store data in the middle of computation, ROM (Read Only Memory) that stores programs, tables, maps, etc. executed by the CPU, driving signals for the speaker 6, HUD 7a, etc. And an output circuit for outputting a display signal and the like. The signals output from the infrared cameras 2R and 2L and the sensors 3, 4 and 5 are converted into digital signals and input to the CPU. Yes.

For example, as shown in FIG. 2, the infrared cameras 2 </ b> R and 2 </ b> L are disposed at the front portion of the host vehicle 10 at a substantially target position with respect to the center in the vehicle width direction of the host vehicle 10. The optical axes of the cameras 2R and 2L are fixed so that they are parallel to each other and the heights from both road surfaces are equal. The infrared cameras 2R and 2L have a characteristic that the output signal level increases (that is, the luminance increases) as the temperature of the object increases.
Further, the HUD 7a is provided so that the display screen is displayed at a position that does not obstruct the driver's front view of the front window of the host vehicle 10.

The vehicle periphery monitoring apparatus according to the present embodiment has the above-described configuration. Next, the operation of the vehicle periphery monitoring apparatus will be described with reference to the accompanying drawings.
Below, the operation | movement of target object detection, such as a pedestrian, and alarm output in the image processing unit 1 is demonstrated.
First, in step S1 shown in FIG. 3, the image processing unit 1 acquires infrared images that are output signals of the infrared cameras 2R and 2L.
Next, in step S2, the acquired infrared image is A / D converted.
Next, in step S3, a grayscale image including halftone information is acquired and stored in the image memory. Here, the right image is obtained by the infrared camera 2R, and the left image is obtained by the infrared camera 2L. Moreover, since the horizontal position on the display screen of the same target object is shifted in the right image and the left image, the distance from the own vehicle 10 to the target object is calculated based on this shift (that is, parallax). Can do.

Next, in step S4, the right image obtained by the infrared camera 2R is used as a reference image, and binarization processing of this image signal, that is, a region brighter than the predetermined luminance threshold ITH is set to “1” (white), and a dark region Is set to “0” (black).
In addition, the process of the following step S4-step S9 is performed about the reference | standard image (for example, right image) obtained by the binarization process.
Next, in step S5, image data obtained by performing binarization processing on the infrared image is converted into run-length data. In the run-length data, an area that has become white by binarization processing is displayed as a line at the pixel level, and each line has a width of one pixel in the y direction and an appropriate number of pixels in the x direction. Is set to

Next, in step S6, the object is labeled in the image data converted into run-length data.
Next, in step S7, the object is extracted according to the labeling of the object. Here, among the lines in the run-length data, when the lines including the equivalent x-direction coordinates are adjacent in the y direction, the adjacent lines are regarded as constituting a single object.
Next, in step S8, the center of gravity G, the area S, and the aspect ratio ASPECT of the circumscribed rectangle are calculated.

Here, the area S is the run length data of the object of the label A (x [i], y [i], run [i], A) (i = 0, 1, 2,..., N−1; If N is an arbitrary natural number), the length (run [i] -1) of each run-length data is calculated by integrating the same object.
The coordinates (xc, yc) of the center of gravity G of the object of label A are the length (run [i] -1) of each run-length data and the coordinates x [i] or y [i] of each run-length data. ] (Ie, (run [i] −1) × x [i] or (run [i] −1) × y [i]) obtained by multiplying the same object. The value is calculated by dividing by the area S.
Further, the aspect ratio ASPECT is calculated as a ratio Dy / Dx between the length Dy in the vertical direction and the length Dx in the horizontal direction of the circumscribed rectangle of the object of label A.
Since the run length data is indicated by the number of pixels (number of coordinates) (= run [i]), the actual length needs to be “−1” (= run [i] −1). Further, the coordinates of the center of gravity G may be replaced by the center of gravity coordinates of a circumscribed rectangle.

Next, the process of step S9 and step S10 and the process of step S11-step S13 are performed in parallel.
First, in step S9, the object is tracked between times, that is, the same object is recognized for each sampling period. In this inter-time tracking, k is a time obtained by discretizing time t as an analog quantity with a sampling period. For example, when objects A and B are extracted at time k, the objects C and D extracted at time (k + 1) are extracted. And the objects A and B are determined. If it is determined that the objects A and B and the objects C and D are the same, the labels of the objects C and D are changed to the labels of the objects A and B, respectively. Then, the position coordinates (for example, the center of gravity, etc.) of each recognized object are stored in an appropriate memory as time series position data.

  Next, in step S10, the vehicle speed VCAR detected by the vehicle speed sensor 4 and the yaw rate YR detected by the yaw rate sensor 3 are acquired, and the yaw rate YR is time integrated to calculate the turning angle θr of the host vehicle 10.

On the other hand, in steps S11 to S13 executed in parallel with the processes of steps S9 and S10, a distance z between the object and the host vehicle 10 is calculated. Since the process of step S11 requires a longer calculation time than the processes of step S9 and step S10, the cycle is longer than that of steps S9 and S10 (for example, about three times the execution cycle of steps S1 to S10). Etc.).
First, in step S11, one object is selected from a plurality of objects tracked on image data obtained by binarizing a reference image (for example, the right image). The entire region surrounding the object with a circumscribed rectangle is extracted from the reference image (for example, the right image) as the search image R1.

  Next, in step S12, a search area for searching for an image (corresponding image) R2 corresponding to the search image R1 from an image (for example, the left image) corresponding to the reference image (for example, the right image) is set and correlated. An operation is executed to extract the corresponding image R2. Here, for example, a search area is set in the left image according to each vertex coordinate of the search image R1, and the luminance difference sum C (a, b) indicating the level of correlation with the search image R1 in the search area. And the region where the total value C (a, b) is minimum is extracted as the corresponding image R2. This correlation calculation is performed not on the image data obtained by the binarization process but on the gray scale image. In addition, when past position data for the same object exists, the search area can be narrowed based on the past position data.

  Next, in step S13, the centroid position of the search image R1, the centroid position of the corresponding image R2, and the parallax Δd in units of the number of pixels are calculated, and the distance between the vehicle 10 and the object, that is, an infrared camera. The distance (object distance) z (m) between the 2R and 2L lenses and the object is the camera base length, that is, the center position of each image sensor of the infrared cameras 2R and 2L, for example, as shown in the following formula (1). Based on the horizontal distance D (m) between them, the camera focal length, that is, the focal length f (m) of each lens of the infrared cameras 2R and 2L, the pixel pitch p (m / pixel), and the parallax Δd (pixel). calculate.

In step S14, after the calculation of the turning angle θr in step S10 and the calculation of the distance z in step S13, for example, as shown in the following formula (2), the object on the image data is calculated. The coordinates (x, y) and the distance z are converted into real space coordinates (X, Y, Z).
Here, the real space coordinates (X, Y, Z), for example, as shown in FIG. 2, are set with the origin O as the midpoint position of the attachment positions of the infrared cameras 2R, 2L at the front of the host vehicle 10, The coordinates on the image data are set with the center of the image data as the origin and the horizontal direction as the x direction and the vertical direction as the y direction. Also, the coordinates (xc, yc) are the coordinates (x, y) on the reference image (for example, the right image) based on the relative positional relationship between the mounting position of the infrared camera 2R and the origin O in the real space. This is a coordinate obtained by converting the origin O of the real space and the center of the image data into coordinates on a virtual image obtained.

  Next, in step S15, the turning angle correction for correcting the positional deviation on the image due to the turning of the host vehicle 10 is performed. This turning angle correction is performed when the host vehicle 10 turns, for example, to the left by the turning angle θr during the period from time k to time (k + 1), on the image data obtained by the infrared cameras 2R and 2L. This is a process for correcting that the range is shifted in the x direction by Δx. For example, as shown in the following formula (3), corrected coordinates (Xr, Yr) obtained by correcting real space coordinates (X, Y, Z) , Zr) are newly set as real space coordinates (X, Y, Z).

Next, in step S16, from the real space position data forming N (for example, about N = 10) time-series data after the turning angle correction obtained within the monitoring period of the predetermined time ΔT for the same object, An approximate straight line LMV corresponding to the relative movement vector between the object and the vehicle 10 is calculated.
In this step S16, the latest position coordinates P (0) = (X (0), Y (0), Z (0)) and the position before (N−1) samples (that is, before a predetermined time ΔT). Coordinate P (N-1) = (X (N-1), Y (N-1), Z (N-1)) is corrected to a position on the approximate straight line LMV, and the corrected position coordinate Pv ( 0) = (Xv (0), Yv (0), Zv (0)) and Pv (N−1) = (Xv (N−1), Yv (N−1), Zv (N−1)). calculate.
Thereby, a relative movement vector is obtained as a vector from the position coordinates Pv (N−1) to Pv (0).
In this way, by calculating an approximate straight line that approximates the relative movement locus of the object with respect to the host vehicle 10 from a plurality of (for example, N) real space position data within the monitoring period of the predetermined time ΔT, the relative movement vector is obtained. It is possible to reduce the influence of the position detection error and accurately predict the possibility of occurrence of contact between the host vehicle 10 and the object.

Next, in step S17, it is determined whether or not the detected object is an alarm target in an alarm determination process according to, for example, the possibility of contact between the detected object and the host vehicle 10 or the like. .
If this determination is “NO”, the flow returns to step S 1 to repeat the above-described steps S 1 to S 17.
On the other hand, if the determination is “YES”, the flow proceeds to step S18.
In step S18, for example, it is determined whether or not an alarm output is necessary in an alarm output determination process according to whether or not the driver of the host vehicle 10 is performing a brake operation based on the output BR of the brake sensor 5. To do.
When the determination result of step S18 is “NO”, for example, when the acceleration Gs generated when the driver of the host vehicle 10 performs a brake operation (deceleration direction is positive) is greater than the predetermined threshold GTH. Determines that the occurrence of contact is avoided by the driver's brake operation, determines, returns to step S1, and repeats the processing of steps S1 to S18 described above.
On the other hand, when the determination result of step S18 is “YES”, for example, when the acceleration Gs (deceleration direction is positive) generated when the driver of the host vehicle 10 performs a brake operation is equal to or less than the predetermined threshold GTH. Alternatively, if the driver of the host vehicle 10 is not performing a brake operation, on the other hand, if the determination result in step S18 is “YES”, it is determined that the possibility of contact is high, and step S19 Proceed to
The predetermined threshold GTH corresponds to a state in which the host vehicle 10 stops when the distance between the object and the host vehicle 10 is equal to or less than the predetermined distance Zv (0) when the acceleration Gs during the brake operation is maintained. The value to be

In step S19, the driver generates an audible alarm such as a sound via the speaker 6, a visual alarm such as a display via the display device 7, or a predetermined tension on the seat belt, for example. A tactile alarm is output by applying a tactilely perceptible tightening force or by generating vibration (steering vibration) that can be perceived by the driver tactilely on the steering wheel.
Next, in step S20, for example, image data obtained by the infrared camera 2R is output to the display device 7, and a relatively approaching object is displayed as an emphasized image.

Hereinafter, the alarm determination process in step S17 described above will be described with reference to the accompanying drawings.
For example, as shown in FIG. 4, the alarm determination process includes a collision determination process, a determination process as to whether or not the vehicle is within the approach determination area, an approach collision determination process, an artificial structure determination process, and a pedestrian determination process. This is a process for determining the possibility of occurrence of contact between the host vehicle 10 and the detected object. In the following, referring to a case where there is an object 20 moving at a speed Vp in a direction substantially orthogonal to the traveling direction of the host vehicle 10 (for example, the Z direction), for example, as shown in FIG. explain.

First, in step S31 shown in FIG. 4, a collision determination process is performed. In this collision determination process, for example, in FIG. 5, the object 20 approaches the distance Zv (0) from the distance Zv (N−1) along the direction parallel to the traveling direction of the host vehicle 10 during the predetermined time ΔT. In this case, the relative speed Vs in the Z direction of the host vehicle 10 is calculated, and when it is assumed that the host vehicle 10 and the object 20 move within the predetermined ground height H while maintaining the relative speed Vs, It is determined whether or not the host vehicle 10 and the object 20 come into contact within the margin time Ts.
If this determination is “NO”, the flow proceeds to step S 37 described later.
On the other hand, if this determination is “YES”, the flow proceeds to step S32.
The margin time Ts is a time for determining the possibility of occurrence of contact at a timing earlier than the predicted contact occurrence time by a predetermined time Ts, and is set to about 2 to 5 seconds, for example. Further, the predetermined ground height H is set to a value that is about twice the vehicle height of the host vehicle 10, for example.

Next, in step S32, it is determined whether or not the detected object exists within a predetermined approach determination area. In this determination process, for example, as shown in FIG. 6, in the area AR <b> 0 that can be monitored by the infrared cameras 2 </ b> R and 2 </ b> L, it is an area before the position Z <b> 1 ahead of the host vehicle 10 by a distance (Vs × Ts). An area of a predetermined ground height H having a width (α + 2β) obtained by adding a predetermined width β (for example, about 50 to 100 cm) to both sides of the vehicle width α of the host vehicle 10 in the vehicle lateral direction (that is, the X direction). If AR1 is present, that is, if the target object continues to exist, it is determined whether or not the target object exists in the approach determination area AR1 where the possibility of contact with the host vehicle 10 is extremely high.
If this determination is “YES”, the flow proceeds to step S 34 described later.
On the other hand, if this determination is “NO”, the flow proceeds to step S33.

And in step S33, the approach collision determination process which determines whether a target object enters into an approach determination area | region and may contact with the own vehicle 10 is performed. In this approach collision determination process, for example, as shown in FIG. 6, objects existing in the entry determination areas AR2 and AR3 of a predetermined ground height H existing outside the approach determination area AR1 in the vehicle lateral direction (that is, the X direction). It is determined whether there is a possibility that the object moves and enters the approach determination area AR1 and contacts the host vehicle 10.
If this determination is “YES”, the flow proceeds to step S 36 described later.
On the other hand, if this determination is “NO”, the flow proceeds to step S 37 described later.

And in step S34, the artificial structure determination process which determines whether a target object is an artificial structure is performed. In this artificial structure determination process, as described later, for example, when a feature that is impossible for a pedestrian is detected, it is determined that the target object is an artificial structure, and is excluded from the alarm target.
If this determination is “NO”, the flow proceeds to step S35.
On the other hand, if this determination is “YES”, the flow proceeds to step S 37.
And in step S35, the pedestrian determination process which determines whether there exists a possibility that a target object is a pedestrian is performed.
If the determination result of step S35 is “YES”, the process proceeds to step S36.
On the other hand, when the determination result of step S35 is “NO”, the process proceeds to step S37 described later.

In step S36, if the object may enter the approach determination area and come into contact with the host vehicle 10 in step S33 described above (YES in step S33), or in step S35, the artificial structure If it is determined that the object determined not to be a pedestrian (YES in step S35), it is determined that contact between the detected object and the vehicle 10 may occur, and the object Is determined to be an alarm target, and a series of processing ends.
On the other hand, in step S37, when there is no possibility that the host vehicle 10 and the object come into contact with each other within the predetermined margin time Ts in step S31 described above (NO in step S31), or the object approaches in step S33. When there is no possibility of entering the determination area and coming into contact with the host vehicle 10 (NO in step S33), or when it is determined in step S34 that the object is an artificial structure (YES in step S34), Alternatively, if it is determined in step S34 that the object determined not to be an artificial structure is not a pedestrian (NO in step S35), there is a possibility of occurrence of contact between the detected object and the host vehicle 10. It is determined that there is no object, it is determined that the object is not an alarm target, and the series of processing ends.

  Hereinafter, as the artificial structure determination process in step S34 described above, a process for determining an artificial structure that has a size and a height position similar to the shape of the pedestrian, particularly the shape of the head, and generates heat will be described. To do.

In this artificial structure determination process, for example, as shown in FIG. 7, a plurality of binarized objects OB and OB are included on a reference image (for example, a right image obtained by the infrared camera 2R), and the vertical direction ( A target area (mask) OA that is an area extending in the height direction and is a target for calculating the luminance state quantity is set.
This mask OA has, for example, a lateral width equivalent to the lateral width Wb of the binarized objects OB and OB, and the vertical width of a predetermined grayscale area (that is, the area where the object exists on the grayscale image). Is set to have the same vertical width.
The average value of luminance values along the horizontal direction (that is, the x direction) at each vertical position J (where J is an appropriate integer) in the vertical direction (that is, the y direction and the vertical direction is the positive direction) ( A change (horizontal average luminance projection) F (J) corresponding to the vertical position J of (horizontal average luminance) is calculated.

Then, it is determined whether or not a predetermined luminance distribution, for example, local maximum positions Hi1 and Hi2 and local minimum positions Low1 and Low2 shown in FIG. 8 exist for the horizontal average luminance projection F (J).
Here, the maximum position Hi1 is, for example, the maximum luminance value in a region from the upper end position of the binarized object detected from the image data converted into run-length data to a position below by a predetermined distance (for example, 80 cm). And the luminance difference value with respect to the upper end position is a predetermined value (for example, 30 gradations) or more.
The minimum position Low1 is a position having a minimum luminance value in a region from the maximum position Hi1 to a position below a predetermined distance (for example, 40 cm), and the luminance difference value with respect to the maximum position Hi1 is a predetermined value ( For example, the position is 30 gradations or more.
Furthermore, the maximum position Hi2 is a position having a maximum luminance value in a region from the minimum position Low1 to a position below a predetermined distance (for example, 80 cm), and the luminance difference value with respect to the minimum position Low1 is a predetermined value ( For example, the position is 30 gradations or more.
The minimum position Low2 is a position having a minimum luminance value in a region from the maximum position Hi2 to a position below a predetermined distance (for example, 40 cm), and the luminance difference value with respect to the maximum position Hi2 is a predetermined value ( For example, the position is 30 gradations or more.

For example, when there are maximum positions Hi1 and Hi2 and minimum positions Low1 and Low2 as the predetermined luminance distribution, the binarized object has two heating elements at predetermined intervals along the vertical direction. It is determined that the man-made structure is arranged, and the series of processes is terminated.
On the other hand, when the predetermined luminance distribution does not exist, it is determined that the binarized object is other than the artificial structure (for example, a pedestrian or the like), and the series of processes is ended.

  As described above, according to the vehicle periphery monitoring device according to the present embodiment, a predetermined area is included in a target area (mask) OA that includes a plurality of binarized objects OB and OB and is a target for calculating a luminance state quantity. By determining whether or not the luminance distribution based on the structural features of the artificial structure exists, it is possible to accurately determine the artificial structure and other than the artificial structure (for example, a pedestrian).

It is a block diagram which shows the structure of the vehicle periphery monitoring apparatus which concerns on embodiment of this invention. It is a figure which shows the vehicle carrying the vehicle periphery monitoring apparatus shown in FIG. It is a flowchart which shows operation | movement of the vehicle periphery monitoring apparatus shown in FIG. It is a flowchart which shows the alarm determination process shown in FIG. It is a figure which shows an example of the relative position of the own vehicle and a target object. It is a figure which shows an example of area | region divisions, such as an approach determination area set ahead of a vehicle. It is a figure which shows an example of object area | region (mask) OA which is an area | region containing two binarization target object OB and OB and is a calculation object of a luminance state quantity. It is a graph which shows an example of the horizontal average brightness | luminance projection F (J) within the mask OA shown in FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image processing unit 2R, 2L Infrared camera 3 Yaw rate sensor 4 Vehicle speed sensor 5 Brake sensor 6 Speaker 7 Display apparatus 10 Own vehicle step S7 Binarization object extraction means step S19 Alarm output means step S34 Area setting means, luminance state quantity calculation Means, luminance distribution determination means, object type determination means Step S35 Pedestrian recognition means

Claims (3)

A vehicle periphery monitoring device that extracts an object existing in the outside of the host vehicle as an object based on an image obtained by imaging by an infrared imaging means,
Binarization object extraction means for extracting a binarization object from image data obtained by binarizing the grayscale image of the image;
On the gray scale image, an area setting unit that sets an area including the binarized object extracted by the binarized object extracting unit;
A horizontal average luminance value calculating means for calculating an average luminance value in the horizontal direction for each vertical position in the vertical direction which is the vertical direction in the area set by the area setting means ;
Determination means for determining whether or not a change according to the position in the vertical direction of the average luminance value calculated by the horizontal average luminance value calculating means is a predetermined change;
An object type determination means for determining the type of the object based on the determination result of the determination means,
The predetermined change has a plurality of maximum positions and minimum positions, and a luminance difference value between the adjacent maximum position and the minimum position is a predetermined value or more . Pedestrian recognition means for recognizing pedestrians existing in the outside world of the vehicle based on the image,
The pedestrian recognition means performs a pedestrian recognition process on the target object that is determined to be other than the predetermined artificial structure by the target object type determination means. Vehicle periphery monitoring device. 3. The alarm output unit according to claim 1, further comprising an alarm output unit configured to output an alarm to the object determined to be other than the predetermined artificial structure by the object type determination unit. Vehicle periphery monitoring device.

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