JP2003217084A - Vehicle detecting method and device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a vehicle detecting method for accurately detecting a vehicle position at a high speed by accurately recognizing a traveling vehicle at a high speed with inexpensive constitution. <P>SOLUTION: This device has (a) a step of storing template data for indicating a characteristic quantity of a contour line for indicating at least a part of a shape of the vehicle, (b) a step (S25) of forming a binarized image for expressing the contour line of the traveling vehicle by imaging and binarizing an image of the traveling vehicle, (c) a step (S41 to 43) of determining whether or not a candidate for the vehicle is included in the binarized image by executing preprocessing on the basis of the binarized image, (d) a step (S27) of extracting characteristic quantity data for indicating the characteristic quantity of the contour line of the binarized image when determining that the candidate for the vehicle is included in the binarized image as a result of the preprocessing, and (e) of a step (S35) of determining that the vehicle is included in the binarized image on the basis of a comparing result by comparing the template data with the characteristic quantity data. <P>COPYRIGHT: (C)2003,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vehicle detection method and apparatus capable of accurately recognizing a running vehicle and accurately detecting its position.

[0002]

2. Description of the Related Art It is necessary to identify a moving vehicle and identify its position, for example, in order to identify a vehicle that violates the speed limit or a vehicle loaded with dangerous goods whose entry into a long tunnel is prohibited. Is. That is, in order to identify these vehicles, the vehicle number on the license plate of these speeding vehicles and the letters on the plate such as "danger,""poison," and "play" posted on vehicles carrying dangerous goods are used. It is necessary to accurately detect the position that can be accurately read, but in order to do so, first the vehicle itself is accurately detected, and the position of the vehicle on the road is detected, and It is necessary to accurately predict the timing at which the plate that indicates that there is is readable.

Conventionally, as a device for detecting characters and the like of such a vehicle, for example, in Japanese Patent Laid-Open No. 2-193266, a device for detecting the entry of the vehicle into each lane and a license plate at a tollgate on a highway. There is shown an apparatus in which an image pickup device for reading is read, and when the entry of a vehicle is detected, an image of the license plate is picked up by the image pickup device to recognize the number.

[0004]

However, in the device disclosed in Japanese Patent Laid-Open No. 2-193266, the speed of the vehicle becomes a certain speed or less, such as a tollgate on an expressway, and each vehicle has one vehicle. It is a license plate recognition device that can detect entry by separating each one, such as a vehicle that slows down the traveling vehicle or travels in multiple lanes, such as the above-mentioned speeding vehicle or tunnel entrance. It cannot be used in places where one cannot be separated separately.

Further, the device disclosed in Japanese Patent Laid-Open No. 2-193266 is provided with a device for detecting the entry of a vehicle and a device for photographing the license plate separately, which inevitably becomes expensive. I don't get it.

Therefore, a method of installing an image pickup device on the road, picking up an image of a traveling vehicle, and reading the license plate and the plates such as "dangerous", "poisonous", and "drama" from the image has been considered. In order to do this, these plates need to be imaged at a certain size or more, and it is necessary to increase the contrast in order to accurately read letters such as numbers and "dangerous", "poison", and "drama". It is necessary to irradiate these plates with a strobe or the like for imaging.

However, a method of continuously illuminating a traveling vehicle with a strobe or the like to pick up an image and recognize the picked-up image when the position is optimum for character recognition requires continuous strobe light emission in a short time. Inevitably, a high-performance strobe is required, and the amount of power consumed becomes enormous, which causes a cost problem.

Therefore, it has been considered that the position of the traveling vehicle is detected by laser or ultrasonic waves, or a loop coil is installed on the road so that the strobe light is emitted by detecting that the vehicle has reached a certain position. However, this method also requires installation space and equipment such as a laser, an ultrasonic wave, and a loop coil, and is inevitably expensive.

Further, in recognizing a vehicle by such an image pickup device, usually, the imaged image 30 shown in FIG. 12 is edge-emphasized and the vehicle is recognized by using the edge, or the vehicle is recognized by a time series difference. Is being done,
In this case, the shadow 50 of the vehicle itself and the shadows of surrounding structures are mistaken for the vehicle, and the vehicle detection accuracy (particularly the position detection accuracy)
There was a problem that it became low.

Therefore, it is an object of the present invention to provide a vehicle detection method and apparatus which can detect a running vehicle at a high speed and accurately with an inexpensive structure and thereby detect the position of the vehicle at a high speed and accurately. .

Japanese Patent No. 2893814 discloses that
The following plate cutting device in the vehicle number automatic reading device is disclosed. That is, in a vehicle number automatic reading device for automatically identifying the plate number of a vehicle running or stopped on the road, a photographing means for photographing the road and a plate frame candidate determining means for judging a plate frame candidate from the photographed image. A character area candidate determination means for determining a character area candidate from a captured image; a portion surrounded by a plate frame estimated based on the plate frame candidate; and a character area estimated based on the character area candidate. And a plate position determining means for determining the plate position on the basis of the degree of overlap.

[0012]

[Means for Solving the Problem] [Means for Solving the Problem] will be described below by using the numbers and symbols used in the [Embodiment of the Invention]. These numbers and signs are
Although added to clarify the correspondence between the description in [Claims] and the description in [Embodiment of the Invention], the technology of the invention described in [Claims] It should not be used to interpret the scope.

The vehicle detection method of the present invention comprises: (a) storing template data indicating a feature amount of a contour line showing at least a part of the shape of the vehicle; and (b) binarizing an image of the traveling vehicle. Generating a binarized image in which the contour line of the traveling vehicle is represented (S25),
(C) a step of performing preprocessing based on the binarized image and determining whether or not a vehicle candidate is included in the binarized image (S41 to 43); and (d) the preprocessing As a result, when it is determined that the vehicle candidate is included in the binarized image, a step of extracting feature amount data indicating a feature amount of the contour line of the binarized image (S27).
And (e) comparing the template data with the feature amount data, and determining that a vehicle is included in the binarized image based on the result of the comparison (S35).
It has and.

In the vehicle detection method of the present invention, in (c), a histogram showing a distribution of binarized data for the binarized image is generated, and the generated histogram and the vehicle stored in advance are stored. Compared with the included binarized image histogram, based on the result of the comparison,
It is determined whether or not the vehicle candidate is included in the binarized image.

In the vehicle detection method of the present invention, the histogram is generated with the X axis and the Y axis of the binarized image as references, and is generated with one of the X axis and the Y axis as a reference. A parameter for evaluating the second histogram generated based on the other of the X-axis and the Y-axis is determined based on the first histogram, and the second parameter is determined based on the determined parameter. And a histogram of a binarized image including the vehicle stored in advance is compared, and the binarized image includes the vehicle candidate based on a result of the comparison. It is determined whether or not there is.

In the vehicle detection method of the present invention, the feature amount of the contour line is represented by a vector amount of the direction and size of the contour line.

In the vehicle detection method of the present invention, as the template data, a feature amount of a contour line indicating at least a part of the shape of the vehicle obtained by executing the vehicle detection method at a plurality of locations is stored.

In the vehicle detection method of the present invention, the feature amount data indicates a feature amount of the contour line included in a partial area of the cut out binarized image.

The vehicle detection device of the present invention includes a storage unit for storing template data indicating a feature amount of a contour line showing at least a part of the shape of the vehicle, an image pickup unit for picking up an image of the traveling vehicle, and the image pickup unit for picking up the image. A binarized image generation unit that binarizes the generated image to generate a binarized image in which the contour of the traveling vehicle is represented, and preprocessing is performed based on the binarized image to generate the binarized image. A pre-processing unit that determines whether or not a vehicle candidate is included in the binary image, and if the result of the pre-processing is that the vehicle candidate is included in the binarized image, the binary value The feature amount extraction unit that extracts the feature amount data indicating the feature amount of the contour of the digitized image, the template data, and the feature amount data are compared, and the binary image is obtained based on the result of the comparison. And a determination unit that determines that the vehicle is included in

[0020]

BEST MODE FOR CARRYING OUT THE INVENTION An embodiment of a vehicle detection method of the present invention will be described with reference to the accompanying drawings.

FIG. 1 is a schematic block diagram of an apparatus showing this embodiment, FIGS. 2 and 3 are flow charts of a vehicle detection method of this embodiment, and FIG. 4 is a pattern feature amount on a screen imaged by an imaging device. FIG. 5 is a diagram for explaining a rectangular area used for calculation, and FIG. 5 is an explanatory diagram when a vehicle is detected. Figure 6
[Fig. 3] is a diagram showing a binarized image. FIG. 7 is a diagram showing a histogram with the y-axis of FIG. 6 as a reference. 8 is shown in FIG.
It is a figure which shows the histogram on the basis of the x-axis of. Figure 9
FIG. 6 is another diagram showing a binarized image. FIG. 10 is a diagram showing a histogram with the y-axis of FIG. 9 as a reference. Figure 11
FIG. 10 is a diagram showing a histogram with the x axis of FIG. 9 as a reference.

In FIG. 1, reference numeral 1 is a road, 2 is a passing vehicle, 3 is a gantry for installing a light source 5 such as a stroboscope for illuminating the vehicle on the road, 4 is a vehicle detection image pickup device, and 6
Is a position on the road 1 that is optimal for capturing an image of the license plate of the vehicle 2.

Further, reference numeral 7 is an analog / digital conversion circuit for analog / digital converting the signal from the image pickup device 4, reference numeral 8 is a data smoothing circuit for removing noise,
Reference numeral 9 is an edge enhancement circuit that enhances the edges of the image by subjecting the input image to differential processing using a Sobel differential filter or the like, and reference numeral 10 is a binarization / storage circuit that binarizes and stores the enhanced image.

Reference numeral 15 is a binarization / storage circuit 10.
It is a vehicle candidate presence / absence determining unit 15 that determines whether or not there is a vehicle candidate in the image from the histogram waveform of the image binarized in.

Reference numeral 11 is a vehicle candidate presence / absence determining unit 1
Check in which direction and to what extent the contour line of the binarized image determined to have a vehicle candidate in 5 has a horizontal component, a vertical component, and a 45 ° component. Then, the pattern feature quantity extraction circuit extracts the pattern feature quantity by expressing it by the vector quantity of the direction and size of the contour line.

Further, reference numeral 12 compares the vehicle shape template of the front shape of the vehicle stored in the vehicle shape template storage device 14 with the pattern feature quantity extracted by the pattern feature quantity extraction circuit 11 to determine the similarity between the two. This is a comparison / collation circuit for calculating.

Further, reference numeral 13 is a threshold value for checking whether the similarity of the pattern feature quantity calculated by the comparison / collation circuit 12 is a certain threshold value or more.

Further, reference numeral 14 expresses a contour line direction pattern feature amount in the front shape of the vehicle by a vector amount of the direction and size of the contour line, stores a certain number of vehicle types as a template, and other patterns. A vehicle shape template storage device that performs statistical processing by a known method, performs deformation processing, and performs processing so that pattern matching can be performed.

Further, reference numeral 16 indicates that when a vehicle is detected, the vehicle is license plate, "danger", "poison",
A signal is sent to the light source light emission control device 17 that controls the light source 5 such as a strobe to emit light when the optimum timing (position) for reading a plate such as "play" is reached, and the signal is obtained. It is a character recognition control circuit that performs control to receive image pickup data from the binarization / storage device 10.

In the present embodiment, for example, a plurality of image pickup devices 4 and a light source 5 such as a strobe are installed on a gantry 3 provided across the road 1, and the plurality of image pickup devices 4 cover the entire width of the road 1. A vehicle traveling at any position on the road 1 can be captured by capturing an image.

When the vehicle 2 is detected by the vehicle detection device of the present embodiment, the vehicle 2 picks up an image pickup position 6 on the road 1 (the image pickup position 6 in the drawing is a temporary frame shown for convenience of explanation. ), The light source 5 such as a strobe is turned on to recognize the characters on the license plate of the vehicle 2 or on the plates such as "danger,""poison," and "drama" posted on vehicles carrying dangerous goods. Recognize the character.

The image pickup device 4 and the light source 5 such as a strobe
Was installed on the gantry 3, but it is obvious that this may be installed on a pillar standing on the roadside.

The image pickup device 4 detects the vehicle 2 on the road 1 by continuously picking up the image of the road 1. The detection is indicated by the bold line in the picked-up image 30 (one frame) shown in FIG. In the detection target area 31, first, the presence / absence of a vehicle candidate is determined from the feature amount of the histogram waveform, and as a result of the determination, it is displayed by a plurality of squares in the vehicle detection target area 31 determined to have the vehicle candidate. Rectangular area 32 for pattern feature quantity calculation that is large enough to cover the front surface of the vehicle
Is cut out and the image data therein is compared with the front pattern template of the vehicle stored in the vehicle shape template storage device 14.

The pattern feature amount calculation rectangular area 32 is cut out in the vehicle feature detection target area 31 in the horizontal (X) direction 33 and the vertical (y) direction 34. One by one, for example, by moving to the position indicated by (n, m), vehicle detection is performed in each of the cut out regions.

Then, this operation is repeated for each frame of the image pickup screen, and when the vehicle 2 is caught at the position of the pattern feature amount calculation rectangular area 32 shown in FIG. 5, for example, vehicle detection is performed at that position. Is determined as.

This determination method will be described with reference to the flow charts shown in FIGS. 2 and 3 and the block diagram of the vehicle detection device of the present embodiment shown in FIG. Note that FIG. 2 and FIG. 3 have a relationship of being connected at the positions of the two-dot chain lines.

First, in step S21, the pattern feature amount calculation rectangular area 32 is set to the origin position (n = 0, m = 0) in the vehicle detection target area 31, so that x = 0 and y = 0.

Next, as shown in step S22, one frame 30 of the image pickup screen from the image pickup device 4 is read, analog / digital conversion is performed by the analog / digital conversion circuit 7, and then smoothing is performed as shown in step S23. The noise component is removed by the smoothing circuit 8 by a known smoothing method.

Next, as shown in step S24, the image within the vehicle detection target area 31 of the image pickup screen (one frame) 30 is edge-enhanced by the edge emphasizing circuit 9 using a known Sobel differential filter or the like. Further, as shown in step S25, the edge-enhanced image is binarized by the binarization / storage circuit 10 and stored. The edge emphasizing circuit 9 extracts only the contour of the vehicle by performing two-dimensional differentiation on the image in which the vehicle or the like is shown. The binarization / storage circuit 10 further binarizes the contour of the vehicle. The binarized image is shown in FIG.

Next, the vehicle candidate presence / absence determining unit 15 extracts the feature amount described below from the image binarized in step S25 (see FIG. 6). The vehicle candidate presence / absence determining unit 15 calculates a horizontal projection histogram as shown in step S41. That is, the vehicle candidate presence / absence determining unit 15 creates a profile (histogram) from the binarized image. Here, the profiles created by the vehicle candidate presence / absence determining unit 15 are shown in FIGS. 7 and 8.

The vertical axis y in FIG. 7 represents the traveling direction of the vehicle. Figure 8
The vertical axis x of is the vehicle width direction. 7 is obtained by adding the number of "1" values (black pixel portions) when the image of FIG. 6 is scanned in the y direction. FIG. 8 is obtained by adding the number of “1” values (black pixel portions) when the image of FIG. 6 is scanned in the x direction.

As shown in FIG. 6, in the vehicle detection target area 31, the vehicle is at the left end (area having a small value in the X direction).
Of the frame in the y direction is almost full (almost the entire area in the y direction). 7 and 8 show an area in which the vehicle is shown in the vehicle detection target area 31.

First, the vehicle candidate presence / absence determining unit 15 detects the vehicle width of the vehicle based on the profile of FIG. In this vehicle width detection processing, a range D in which the number of pixels added by scanning in the X direction exceeds the threshold value C is determined as the vehicle width. When the vehicle width is detected, the parameter for detecting the feature amount from the histogram of FIG. 7 is determined (selected) based on the vehicle width. For example, since the vehicle widths of the large vehicle and the small vehicle are different, if the portion of the histogram to be extracted as the feature amount is determined based on the vehicle width, the large vehicle and the small vehicle can be further separated. Accurate vehicle candidate detection can be performed.

In the vehicle width detection process, based on the histogram of FIG. 8, for the image of FIG.
It is recognized that the image of the vehicle is included in the range of 0 ≦ x ≦ D of 1. The vehicle width detected by the vehicle width detection process (D-
0 = D) based on the value y in the vehicle detection target area 31.
In the direction, it is recognized that the vehicle is photographed over almost the entire area. As a result, in the histogram of FIG. 7, 0 ≦
A parameter for extracting a region in the range of y ≦ B as a feature amount is set. With the set parameters, the waveform (waveform of the histogram) in the range of 0 ≦ y ≦ B in FIG. 7 is calculated as the feature amount of step S42.

On the other hand, for the image of FIG. 9, for example, FIG.
Based on the histogram of No. 1, it is recognized that the vehicle image is included in the range of x1 ≦ x ≦ x2 of the vehicle detection target area 31. The vehicle width detected by the vehicle width detection process (F =
Based on the value of (x2-x1), it is recognized that the vehicle is photographed in the range E from y1 to y2 in the y direction in the vehicle detection target area 31. As a result, parameters for extracting the region in the range of y1 ≦ y ≦ y2 as the feature amount in the histogram of FIG. 10 are set. According to the set parameters, y1 ≦ y ≦ y in FIG.
The waveform in the range of 2 (waveform of histogram) is step S4.
It is calculated as a feature amount of 2.

Next, as shown in step S43, the feature amount obtained in step S42 is compared with the waveform in the y-axis direction indicating the leading image of the vehicle. It is determined that a vehicle candidate is detected in the vehicle detection target area 31 (step S43-Y), and the steps from step S26 onward are executed. In contrast,
As a result of the comparison in step S43, if the similarity between the two is less than the threshold value, it is determined that no vehicle candidate is detected in the vehicle detection target area 31 (step S43-
N), the steps following step S26 are not executed. Since the pattern recognition process from step S26 onward has a heavy load, the presence / absence of a vehicle candidate is determined based on the feature amount extracted from the histogram. If no vehicle candidate is detected, the pattern recognition process with a large load is omitted. Then
Speed up the whole process. The vehicle candidate detection process based on the feature amount extracted from the histogram has a small amount of calculation, and thus is suitable as preprocessing.

The case where a vehicle candidate is detected in step S43 will be described below.

In the next step S26, the rectangular area 32 for calculating the pattern feature quantity at the origin position of the vehicle detection target area 31 of FIG. 4 in which the vehicle candidate is detected is cut out. next,
In step S27, the pattern feature amount extraction circuit 11 extracts the feature amount of the pattern in the pattern feature amount calculation rectangular area 32. This is to check in which direction and to what extent each of the contour lines of the pattern in which the edges in the pattern feature amount calculation rectangular area 32 are emphasized, that is, to see the vector amount of the direction and size of the contour line. Pattern feature amount is calculated by using, for example, the horizontal component of the contour line,
The magnitudes of the vertical direction component and the 45 ° direction component are calculated to obtain pattern feature amount data.

In step S27, after the pattern feature amount data in the first rectangular region 32 for pattern feature amount calculation is extracted, as shown in step S28, comparison / comparison is performed.
The collation circuit 12 compares and collates the extracted pattern feature amount data with the vehicle shape template of the front shape of the vehicle stored in the vehicle shape template storage device 14 to calculate and store the degree of similarity. Since this similarity calculation is a value represented by a vector quantity indicating in which direction and in what extent the pattern feature amount data is present as described above, the front surface of the vehicle stored in the vehicle shape template storage device 14 is calculated. It can be easily calculated by comparing with a value indicating the vector amount of the contour line in the vehicle shape template of the shape.
Moreover, since the pattern feature amount data is a value indicating the vector amount of the contour line, the similarity can be calculated regardless of the size of the captured image.

In this way, when the similarity between the pattern feature quantity data in the first pattern feature quantity calculation rectangular area 32 and the vehicle shape template is calculated and stored, the pattern feature quantity of the next pattern feature quantity calculation rectangular area 32 is stored. In order to calculate, the pattern feature amount calculation rectangular area 32 is moved by x + in one step in the x direction in step S29.
1 is calculated, and it is determined in step S30 whether the value of x does not exceed the final position coordinate n of the vehicle detection target area 31. In this case, since x is 2, the processing is step S26.
Returning to step S27, the process from step S27 to step S29 described above is repeated. If the value of x exceeds n in step S30, the process goes to step S31.

Then, in step S31, x is set to 0, and in step S32, y + 1 is calculated to move the rectangular area 32 for pattern feature calculation in the y direction by one box, and the value of y is calculated in step S33. It is determined whether the vehicle candidate does not exceed the final position coordinate m of the vehicle detection target area 31 in which the vehicle candidate is detected. In this case, y is 2, so the process returns to step S26, and step S27 described above is executed.
The processing from step S32 is repeated from step S3
When the value of y exceeds m in 3, the process goes to step S34.

Then, in this step S34, the pattern feature amount calculation rectangular area 32 showing the highest similarity between the pattern feature amount data stored in the comparison / collation circuit 12 and the vehicle shape template is selected, and the step This similarity is compared with the threshold 13 in S35.
As a result, when the similarity is higher than the threshold 13, it is determined that the vehicle 2 exists in the pattern feature amount calculation rectangular area 32, a vehicle detection flag is set in step S36, and the vehicle detection result is character recognition control in step S37. It is sent to the circuit 16. When the data for one frame is processed in this manner, the process returns to the first step S21, and the vehicle detection operation described above is repeated.

On the other hand, the character recognition control circuit 16 to which the vehicle detection result is sent receives the pattern feature amount calculation rectangular area in which the vehicle 2 exists in the vehicle detection target area 31 stored in the binarization / storage circuit 10. 32, the size of the vehicle 2 is determined to determine whether it is the optimum timing (position) for reading a license plate or a plate such as “danger,” “poison,” or “play”. If it is determined that the characters are too small in the reading of the plate at this position, the size of the vehicle 2 and the position where the vehicle 2 is detected in the next frame or in the next frame, and the size of the vehicle 2 in the previous frame. The speed of the vehicle 2 is calculated according to the change from the position or size of the vehicle 2, and the license plate or "danger",
The optimum timing (position) for reading a plate such as "poison" or "drama" is calculated, and a signal is sent to the light source emission control circuit 17.

Therefore, the light source light emission control device 17 is installed in the vehicle 2
The light source 5 such as a strobe light is emitted at the optimum timing (position) to read the license plate of "No", "dangerous", "poison", "drama", etc. Smoothing circuit 8 through 7
After being smoothed by, the character plate is sent to the character recognition control circuit 16 and the character recognition circuit (not shown) recognizes the license plate of the vehicle 2 and characters such as “danger”, “poison”, and “play”.

By thus configuring the vehicle detection method and apparatus, the vehicle candidate detection processing by the horizontal projection of the vehicle edge image and the processing by the shape pattern detection of the vehicle front surface to which the pattern recognition technology is applied are combined. The vehicle can be detected accurately and at high speed.
That is, the candidate position of the vehicle can be detected at high speed by the horizontal projection of the vehicle edge image, and accurate vehicle detection can be realized by using the shape pattern feature of the front surface of the vehicle.

In the above embodiment, the vehicle shape template of the front shape of the vehicle stored in the vehicle shape template storage device 14 is registered in the vehicle shape template storage device 14 when the vehicle detection device of this embodiment was initially installed. In addition to the vehicle shape template, the shape pattern of the vehicle detected after installation can be automatically registered at any time.
This improves the detection accuracy.

Further, the vehicle detection device of the present embodiment is installed at a large number of points, the devices installed at the large number of points are mutually connected by a network, and the vehicle shape patterns obtained by the respective devices are mutually connected. The detection accuracy is improved by automatically registering in each device by reflecting the above. In this case, the vehicle shape pattern obtained by each device is collectively processed and managed by the host computer, and the host computer can transmit the data to each device.

Further, according to this embodiment, the vehicle 2 can be operated by the single image pickup device 4 without separately providing a vehicle position detecting device using a laser, an ultrasonic wave, a loop coil or the like.
Timing (position) for recognizing information, reading license plates and plates such as "danger,""poison," and "play"
And calculation of image data for recognizing these characters can be performed. Moreover, the recognition of the vehicle is
By using the contour direction pattern feature amount, it can be performed even if the vehicle is distant, and the shadow 50 of the vehicle itself or the shadow of the building as shown in FIG. 12 is mistaken for the vehicle. No, the license plate of the vehicle or "danger",
It is possible to provide a vehicle detection method and device that are optimal for recognizing characters such as “poison” and “play”.

In the above description, the analog / digital conversion circuit 7, the smoothing circuit 8, the edge emphasizing circuit 9, the binarization / storage circuit 10, for processing the signal from the image pickup device 4,
It has been described that the pattern feature amount extraction circuit 11, the comparison / collation circuit 12, the threshold 13, the vehicle shape template storage device 14, the vehicle candidate presence / absence determination unit 15, the character recognition control circuit 16, the light source emission control device 17, and the like are used. It goes without saying that circuits other than the analog / digital conversion circuit 7 may be configured using a computer and its storage device.

[0060]

According to the vehicle detection method of the present invention, it is possible to quickly and accurately recognize a running vehicle with an inexpensive structure, and thereby to quickly and accurately detect the position of the vehicle.

[Brief description of drawings]

FIG. 1 is a schematic block diagram of an apparatus showing an embodiment of a vehicle detection method of the present invention.

FIG. 2 is a flowchart showing a part of the vehicle detection method of the present embodiment.

FIG. 3 is a flowchart showing another part of the vehicle detection method of the present embodiment.

FIG. 4 is a diagram for explaining a rectangular area used for pattern feature amount calculation on a screen imaged by an imaging device in the present embodiment.

FIG. 5 is an explanatory diagram when a vehicle is detected in the present embodiment.

FIG. 6 is a diagram showing a binarized image in the present embodiment.

7 is a diagram showing a histogram based on the y-axis of FIG. 6 in the present embodiment.

8 is a diagram showing a histogram based on the x-axis of FIG. 6 in the present embodiment.

FIG. 9 is another diagram showing a binarized image in the present embodiment.

FIG. 10 is a diagram showing a histogram based on the y-axis of FIG. 9 in the present embodiment.

11 is a diagram showing a histogram based on the x-axis of FIG. 9 in the present embodiment.

FIG. 12 is a diagram for explaining a defect in the conventional vehicle detection device.

[Explanation of symbols]

1 road 2 passing vehicles 3 gantry 4 Vehicle detection imaging device 5 light sources 6 Imaging position on the road 7 Analog / digital conversion circuit 8 Smoothing circuit 9 Edge enhancement circuit 10 Binarization / memory circuit 11 Pattern feature extraction circuit 12 Comparison / collation circuit 13 threshold 14 Vehicle shape template storage device 15 Vehicle candidate existence determination unit 16 character recognition control circuit 17 Light source emission control device

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Jun Kuroda             2-1-1 Niihama, Arai-cho, Takasago, Hyogo Prefecture             Takasago Laboratory, Mitsubishi Heavy Industries, Ltd. F-term (reference) 5B057 AA16 BA02 CA02 CA08 CA12                       CA16 CB02 CB08 CB12 CB16                       CC01 CE12 DB02 DB05 DB09                       DC16 DC19                 5C054 CB03 FC12 FC14 FC15 FC16                       GB13 HA26                 5H180 AA01 CC04 DD02                 5L096 EA43 FA05 FA06 FA36 FA67                       JA03 JA09 JA11 JA18

Claims (7)

[Claims] 1. A step of storing template data indicating a feature amount of a contour line showing at least a part of a shape of a vehicle, and (b) an image of the traveling vehicle being picked up and binarized to obtain the image of the traveling vehicle. Generating a binarized image in which a contour line is represented, and (c) performing preprocessing based on the binarized image to determine whether the binarized image includes a vehicle candidate. And (d) as a result of the pre-processing, when it is determined that the vehicle candidate is included in the binarized image, a feature indicating a feature amount of the contour line of the binarized image. Extracting the quantitative data, (e)
A vehicle detection method comprising: comparing the template data with the feature amount data, and determining that the vehicle is included in the binarized image based on a result of the comparison. 2. The vehicle detection method according to claim 1, wherein (c) generates a histogram showing a distribution of binarized data on the binarized image, and stores the histogram and the pre-stored histogram. A vehicle detection method that compares a histogram of a binarized image including the generated vehicle and determines whether or not the vehicle candidate is included in the binarized image based on a result of the comparison. 3. The vehicle detection method according to claim 2, wherein the histogram is generated with reference to the X axis and the Y axis of the binarized image, and one of the X axis and the Y axis is used as a reference. First generated as
Of the X-axis and the Y-axis based on the histogram, the parameters for evaluating the second histogram are determined, and the second histogram is determined based on the determined parameters. Is compared with the histogram of the binarized image including the vehicle stored in advance, and whether or not the vehicle candidate is included in the binarized image based on the result of the comparison. Vehicle detection method to determine whether. 4. The vehicle detection method according to any one of claims 1 to 3, wherein the feature amount of the contour line is represented by a vector amount of the direction and size of the contour line. 5. The vehicle detection method according to claim 1, wherein the template data is at least a part of a vehicle shape obtained by executing the vehicle detection method at a plurality of locations. The vehicle detection method in which the feature amount of the contour line indicating the is stored. 6. The vehicle detection method according to claim 1, wherein the feature amount data is a feature of the contour line included in a partial region of the cut out binarized image. Vehicle detection method that indicates quantity. 7. A storage unit for storing template data showing a feature amount of a contour line showing at least a part of the shape of a vehicle, an image pickup unit for picking up an image of a traveling vehicle, and binarizing the picked-up image. A binarized image generation unit that generates a binarized image in which the contour of the traveling vehicle is represented, and preprocessing is performed based on the binarized image, and the binarized image includes vehicle candidates. A pre-processing unit that determines whether or not the vehicle has been selected, and, as a result of the pre-processing, when it is determined that the vehicle candidate is included in the binarized image, the contour line of the binarized image. A feature amount extraction unit that extracts feature amount data indicating the feature amount, the template data, and the feature amount data are compared, and if the vehicle is included in the binarized image based on the result of the comparison. A vehicle detection device including a determination unit for determining.