Source: https://patents.google.com/patent/JP6473571B2/en
Timestamp: 2020-01-29 02:15:39
Document Index: 387035193

Matched Legal Cases: ['art 12', 'art 14', 'art 21', 'art 18', 'art 23', 'art 24', 'art 25', 'art 41']

JP6473571B2 - TTC measuring device and TTC measuring program - Google Patents
TTC measuring device and TTC measuring program Download PDF
JP6473571B2
JP6473571B2 JP2014060739A JP2014060739A JP6473571B2 JP 6473571 B2 JP6473571 B2 JP 6473571B2 JP 2014060739 A JP2014060739 A JP 2014060739A JP 2014060739 A JP2014060739 A JP 2014060739A JP 6473571 B2 JP6473571 B2 JP 6473571B2
JP2014060739A
JP2015182604A (en
JP2015182604A5 (en
正憲 小崎
2014-03-24 Application filed by アルパイン株式会社, 東芝デバイス＆ストレージ株式会社, 東芝デジタルソリューションズ株式会社 filed Critical アルパイン株式会社
2014-03-24 Priority to JP2014060739A priority Critical patent/JP6473571B2/en
2015-10-22 Publication of JP2015182604A publication Critical patent/JP2015182604A/en
2016-12-28 Publication of JP2015182604A5 publication Critical patent/JP2015182604A5/ja
2019-02-20 Publication of JP6473571B2 publication Critical patent/JP6473571B2/en
238000010187 selection method Methods 0 description 12
Embodiments described herein relate generally to a TTC measurement apparatus and a TTC measurement program.
Recently, as a technique for preventing a predetermined object such as a vehicle such as a car or a streetlight on a shoulder from colliding with an object that is relatively close to the predetermined object, the predetermined object and the target object are used. Development of a technique for predicting a time until collision (TTC: Time To Collision) is desired. In this type of technology, for example, a time until a collision between a vehicle and an object is predicted based on an image captured by a peripheral monitoring camera such as a camera mounted on a vehicle or a camera fixed to a streetlight. There is technology. When using an image picked up by an in-vehicle camera, it is possible to use digitized image data as compared with the case of using a radar, so that it is possible to make a complicated determination such as an approach angle of an object.
Conventionally, as this type of technique, for example, a technique for predicting a time TTC until a collision occurs based on an enlargement ratio of an object in a captured image (see Patent Document 1), a position of the ground in the captured image and the object For example, there is a technique for predicting a time TTC until the collision based on the position (see Patent Documents 2 and 3).
JP 2006-107422 A JP 2006-133125 A JP 2012-98796 A
On the other hand, image processing technology for detecting an object in a captured image has been remarkably developed in recent years in order to shorten the time required for detection while improving detection accuracy. Examples of this type of object detection technique include a technique using a HOG (Histogram of Oriented Gradients) feature.
In object detection processing using the HOG feature (hereinafter referred to as HOG processing), a plurality of images (hereinafter referred to as pyramid images) obtained by enlarging and reducing one captured image at a predetermined time are prepared. An object can be detected by scanning a frame of the same size for each.
Each of the plurality of images constituting the pyramid image is an image having a different enlargement / reduction ratio, and the image of the object is included in each of the plurality of images in different sizes. For this reason, in the technology for detecting an object using a pyramid image, among the component images of the pyramid image, the distance from the enlargement / reduction ratio of the component image in which the sizes of the scanned frame and the object substantially coincide with each other to the object Can be roughly predicted.
However, since the respective enlargement / reduction ratios of the constituent images of the pyramid image are discrete values, it is difficult to accurately predict the time TTC until the collision by the technique of detecting an object using the pyramid image. Moreover, even if the technique described in Patent Document 1 is applied to a technique for detecting an object using a pyramid image, the enlargement ratio of the object becomes a jump value, so that TTC is accurately predicted. Is difficult. Moreover, when using the position of the ground like the technique of patent document 2 or 3, the error in a distant position will become very large.
TTC measuring apparatus according to an embodiment of the present invention, in order to solve the problems described above, each time for acquiring images from a camera provided in a vehicle, that generates pyramid images acquired image as the reference image and generate section, the the pyramid images are generated for each of all the images forming the pyramid images, while moving the model part to be compared of the detection object in an image by scanning the image The likelihood that the part of the pyramid image group among the scanning unit that acquires the position of the part in the image when the likelihood of the part with respect to the model is greater than or equal to the threshold is greater than or equal to the threshold for an image of the part in present, we obtain a ratio to the reference image, based on the magnification calculated estimated distance between the detection object and the vehicle, the last time Using the previous estimated distance obtained when the image was acquired from the camera, the obtained estimated distance is corrected to obtain the corrected estimated distance, and when the predetermined condition is satisfied, the current corrected A selection unit that stores the estimated distance in the storage unit in association with the acquisition time when the image is acquired from the camera this time, a plurality of the corrected estimated distances stored in the storage unit, and a plurality of the corrected estimated distances a calculation unit in which the acquisition time and the vehicle based on the said detection object and out calculate the time to collision is, are those having the said selection unit constitutes the pyramid images For each image, a range in which the magnification with respect to the reference image has a predetermined width is set as the belonging range of the image. (1) The magnification corresponding to the corrected estimated distance of this time belongs to one belonging range, and If the magnification corresponding to the corrected estimated distance stored in the storage unit does not belong to the one belonging range, or (2) the magnification corresponding to the current corrected estimated distance and the previous corrected estimated distance When the plurality of affiliation ranges of each image are included between the magnification corresponding to, the current estimated correction distance is stored in the storage unit.
1 is a block diagram showing a configuration example of an image processing apparatus according to a first embodiment of the present invention. The CPU of the control unit shown in FIG. 1 accurately predicts the TTC of the detected detection object while detecting the detection object included in the processed image using the pyramid image generated from the image acquired by the camera. The flowchart which shows the outline of the procedure at the time of doing. Explanatory drawing which shows an example of the predetermined range set when the dictionary production | generation part produces | generates the dictionary. Explanatory drawing which shows an example of the visual axis of each camera, and a projection surface in case a camera is provided in the right and left and the rear part, and detects the front surface of the other vehicle which runs in parallel with the own vehicle. Explanatory drawing which shows an example of the pyramid image produced | generated by the process image production | generation part. The figure for demonstrating the scanning frame in case a detection target object is a motor vehicle. (A) is explanatory drawing which shows an example of the normalized image which used the focal distance as f, (b) is a mode in case the detection target object is a person and the vertical width Oh of a scanning frame is classify | categorized into 3 types. Explanatory drawing which shows an example. (A) is explanatory drawing which shows an example of the focus position image which detected the person standing on the ground, (b) is a figure for demonstrating the calculation method of the lower end Hyb of the detection frame in the example shown to (a). . Explanatory drawing which shows an example of the focus position image which detected the person who floated only predetermined height from the ground. 3 is a subroutine flowchart illustrating an example of a procedure of TTC calculation processing executed by a TTC calculation unit in step S7 of FIG. The figure for demonstrating the 1st selection method of an effective distance. The figure for demonstrating the 2nd selection method of an effective distance. (A) is explanatory drawing which shows an example of the focus position image for demonstrating the 3rd selection method of effective distance, (b) is distance Dz from the own vehicle in the example shown to (a) to a detection target object. The figure for demonstrating the calculation method. The figure for demonstrating the TTC calculation method based on an effective distance. The figure for demonstrating the method of recalculating TTC when the relative speed of a detection target object and the own vehicle falls. (A) is explanatory drawing which shows an example of a focus position image in case the lower end of a detection frame exists only Dy from the ground, (b) is a processing image using recalculation distance Dz 'in the example shown to (a). The figure for demonstrating the method to correct the position of the upper detection frame.
Embodiments of a TTC measurement device and a TTC measurement program according to the present invention will be described with reference to the accompanying drawings, taking an image processing device and an image processing program as examples .
FIG. 1 is a block diagram showing a configuration example of an image processing apparatus 10 according to the first embodiment of the present invention.
The image processing apparatus 10 includes a camera 11, a control unit 12, a storage unit 13, a vehicle information acquisition unit 14, a lighting device 16, a horn 17, a speaker 18, and a display device 19.
The camera 11 is composed of a CCD (Charge Coupled Device) image sensor or a CMOS (Complementary Metal Oxide Semiconductor) image sensor. The camera 11 captures an image around a vehicle such as a private car and generates image data and supplies the image data to the controller 12.
For example, when monitoring the rear, the camera 11 is provided in the vicinity of the number plate at the rear of the vehicle or the upper part of the rear window (rear camera). When monitoring the side of the vehicle, the camera 11 is provided in the vicinity of the side mirror (side camera). When monitoring the front of the vehicle, the camera 11 is provided in the vicinity of the number plate at the front of the vehicle or in the upper part of the front window (front camera).
The camera 11 may be attached with a wide-angle lens or a fish-eye lens so as to enable wide-angle imaging. For example, when the camera 11 capable of wide-angle imaging is arranged near the side mirror (side camera), it is possible to simultaneously image the front and rear of the vehicle in addition to the side of the vehicle. In addition, a wide range of vehicle surrounding images may be captured by using a plurality of cameras 11.
In the following description, an example in which a wide-angle lens or a fish-eye lens is attached to the camera 11 so as to enable wide-angle imaging is shown.
The control part 12 is comprised by the microcontroller provided with CPU, RAM, and ROM, for example. The CPU of the control unit 12 loads an image processing program stored in a storage medium such as a ROM and data necessary for executing the program into the RAM, and is generated from an image acquired by the camera 11 according to the program. The process for accurately predicting the TTC (time until collision) of the detected detection object is executed while detecting the detection object included in the processed image using the pyramid image.
The RAM of the control unit 12 provides a work area for temporarily storing programs and data executed by the CPU. A storage medium such as a ROM of the control unit 12 stores an image processing program and various types of data necessary for executing these programs.
A storage medium such as a ROM has a configuration including a recording medium readable by a CPU, such as a magnetic or optical recording medium or a semiconductor memory, and a part of programs and data in the storage medium. Or you may comprise so that all may be downloaded via an electronic network via the network connection part which is not shown in figure.
In this case, the network connection unit implements various information communication protocols according to the form of the network, and the control unit 12 and electric devices such as ECUs of other vehicles are connected via the electronic network according to the various protocols. Connecting. For this connection, an electrical connection via an electronic network can be applied. Here, an electronic network means an information communication network using telecommunications technology in general, in addition to a wireless / wired LAN (Local Area Network) and the Internet network, a telephone communication line network, an optical fiber communication network, a cable communication network, Includes satellite communications networks.
The storage unit 13 is a non-volatile memory in which data can be read and written by the control unit 12, and stores various types of information such as an image dictionary (model) generated in advance using an image obtained by imaging the detection target. Yes. These pieces of information may be updated via an electronic network or a portable storage medium such as an optical disk.
The vehicle information acquisition unit 14 acquires at least information on the current acceleration of the host vehicle and outputs the information to the control unit 12. The vehicle information acquisition part 14 may be comprised, for example by an acceleration sensor, and may have a vehicle information acquisition function generally used in CAN (Controller Area Network). In the present embodiment, the vehicle information acquisition unit 14 is not necessarily provided.
The lighting device 16 is configured by a general headlight, and is controlled by the control unit 12 to perform blinking (so-called passing), for example, to warn the outside of the host vehicle.
The horn 17 is controlled by the control unit 12 and outputs a warning sound to the outside of the host vehicle.
The speaker 18 is provided in the vehicle of the host vehicle, and is controlled by the control unit 12 to output sound corresponding to various information such as a beep sound and information for notifying the driver that the danger is imminent. Output.
The display device 19 is provided at a position where the driver can visually recognize, and a display output device such as a general vehicle-mounted display, a car navigation system, or a HUD (head-up display) can be used. Accordingly, various information such as an image captured by the camera 11 and an image indicating the detection position of the detection target are displayed.
(Outline of configuration and operation of control unit 12)
Next, an outline of the configuration and operation of the function realization unit by the CPU of the control unit 12 will be described.
As illustrated in FIG. 1, the CPU of the control unit 12 functions as at least a dictionary generation unit 21, a processed image generation unit 22, a detection unit 23, a TTC calculation unit 24, and a warning unit 25 according to an image processing program. The detection unit 23 includes a HOG processing unit 31, a HOG result determination unit 32, and a HOG result selection unit 33.
Each unit 21-25 uses a required work area in the RAM as a temporary storage location for data. In addition, you may comprise these function implementation parts by hardware logics, such as a circuit, without using CPU.
FIG. 2 shows a detection target detected by the CPU of the control unit 12 shown in FIG. 1 while detecting the detection target included in the processed image using the pyramid image generated from the image acquired by the camera 11. It is a flowchart which shows the outline of the procedure at the time of accurately predicting TTC (time to collide). In FIG. 2, reference numerals with numerals added to S indicate steps in the flowchart.
In step S <b> 1, the dictionary generation unit 21 generates an image dictionary (model) using an image obtained by capturing an object to be detected in advance and stores the image dictionary (model) in the storage unit 13. For example, the dictionary generation unit 21 sets the detection target in a predetermined range within the imaging range of the camera 11 so that a predetermined visual axis of the camera 11 and a normal direction of the detection target surface of the detection target are parallel to each other. One dictionary is generated in advance using images obtained by imaging the detection object in advance at each of a plurality of positions by the camera 11 at a plurality of positions.
The dictionary generated by the dictionary generation unit 21 has contents applicable to various techniques for detecting an object from an image based on a feature amount. In the following description, the dictionary generated by the dictionary generation unit 21 is a dictionary (hereinafter referred to as HOG dictionary) suitable for object detection processing (hereinafter referred to as HOG processing) based on the HOG feature value, and the detection unit 23 performs HOG processing. An example of performing the above will be described.
Next, in step S <b> 2, the processed image generation unit 22 acquires a captured image obtained by capturing the periphery of the vehicle with a wide angle of view from the camera 11.
Next, in step S <b> 3, the processed image generation unit 22 is based on an image captured by the camera 11, and an image group (pyramid image) configured by a plurality of processed images having different projection plane distances set from the own vehicle 41. ) Is generated. Each processed image is generated as an image that is perspective-projected on a projection plane whose normal direction is the same visual axis as the visual axis used for generating the dictionary. Each processed image is associated in advance with an image distance as an estimated distance from the host vehicle 41.
Next, in step S4, the HOG processing unit 31 of the detection unit 23 performs HOG processing (see, for example, Japanese Patent Application Laid-Open Nos. 2010-44438 and 2010-55195) and outputs the likelihood. Specifically, the HOG processing unit 31 converts the processed image into a gradient direction image, scans a frame of a predetermined size according to the detection target, and obtains an HOG feature amount for each scanning position using the HOG dictionary. Thus, the likelihood indicating the likelihood that the detection target exists at each scanning position is obtained. The HOG processing unit 31 can obtain the likelihood (score) by applying the in-frame image at each scanning position to the dictionary. It can be said that the higher the likelihood, the more the image matches the dictionary.
The HOG feature value is a quantification of how many vertical, horizontal, and diagonal edges exist in a block. For this reason, it is known that it is not easily affected by changes in brightness and the like, and is robust against changes in shape. The HOG process by the HOG processing unit 31 may be a process using co-occurrence (coHOG process).
Next, in step S <b> 5, the HOG result determination unit 32 of the detection unit 23 considers the result that the likelihood obtained by the HOG processing unit 31 is greater than the threshold as an effective result. The number of scanning frames that are effective results by the HOG result determination unit 32 as the pixel shift amount when the HOG processing unit 31 scans the scanning frame in the HOG processing is reduced and the likelihood threshold is reduced. Will increase.
Next, in step S <b> 6, the HOG result selection unit 33 of the detection unit 23 calculates the simplest result of the highest likelihood among the plurality of effective results obtained by the HOG result determination unit 32 or the plurality of effective results. One of the most effective results is selected by averaging, weighted average by likelihood, or the like. Further, the HOG result selection unit 33 may directly output the estimated distance from the own vehicle associated with the same or closest processed image from the selected most effective result, but here the reference image ( A frame (hereinafter referred to as a detection frame) in which the size and position of the most effective result frame selected on the reference image (hereinafter referred to as a normalized image) is normalized is output. That is, the far frame is converted into a small frame, and the short frame is converted into a large frame. Note that step S6 may not be executed when there is one scanning frame that is determined to be an effective result by the HOG result determination unit 32.
Next, in step S7, the TTC calculation unit 24 obtains an estimated distance from the own vehicle to the detection object based on the relationship between the normalized image and the size of the detection frame, and associates the estimated distance with the plurality of processed images. Based on each image distance as an estimated distance from the own vehicle (hereinafter collectively referred to as a pyramid image distance as appropriate), a time TTC until the own vehicle and the detection target collide is obtained and output. At this time, when the number of detected frames is one, or when one result having the highest likelihood is selected, the distance of the detection frames matches one of the pyramid image distances.
The distance from the own vehicle associated with each of the plurality of processed images is a jump value. The TTC calculation unit 24 can obtain a more accurate TTC by using the estimated distance history and the pyramid image distance instead of using the estimated distance as it is.
Next, in step S <b> 8, the warning unit 25 determines whether or not to notify the driver of the own vehicle of the estimated distance from the own vehicle to the detection target and the TTC information output by the TTC calculation unit 24. . When the output information of the TTC calculation unit 24 is to be notified, the process proceeds to step S9. On the other hand, when it is not necessary to notify, it progresses to step S10. For example, it may be determined that the user should be notified when the position of the detection target is within a predetermined distance from the host vehicle or when the TTC is within a predetermined time. This step S8 may not be executed.
Next, in step S <b> 9, the warning unit 25 outputs the output information of the TTC calculation unit 24 by at least one of voice output and buzzer output via the speaker 18 and warning display to the display device 19 for the driver of the vehicle. To the driver of the host vehicle, and the process proceeds to step S10. For example, the warning unit 25 superimposes the normalized image and the image indicating the distance from the vehicle to the detection target and the information on TTC and displays them on the display device 19. In addition, the warning unit 25 may notify the outside of the host vehicle by a blinking of the lighting device 16 (so-called passing) or a warning sound of the horn 17.
Next, in step S10, the control unit 12 determines whether or not to end a series of procedures. For example, when the own vehicle is stopped for a predetermined time or more or when there is an input instruction from the driver, the series of procedures is completed. When continuing the procedure, the process returns to step S2, the image of the next frame is acquired from the camera 11, and the processing of steps S3-S9 is repeated.
With the above procedure, the detection target contained in the processed image is detected using the pyramid image generated from the image acquired by the camera 11, and the TTC (time until collision) of the detected detection target is detected. Predict with high accuracy.
(Dictionary generation)
Here, the dictionary produced | generated by the dictionary production | generation part 21 which concerns on this embodiment is demonstrated.
FIG. 3 is an explanatory diagram illustrating an example of the predetermined range 44 set when the dictionary generation unit 21 generates a dictionary.
In the present embodiment, a processed image of the line of sight axis 40 is generated using the wide-angle camera 11 and facing directly in parallel with the traveling direction of the vehicle 41 (see FIG. 3). The dictionary generation unit 21 according to this embodiment can detect a detection target 42 with a positional relationship between the visual axis 40 and the detection target surface normal direction as shown in FIG. 40 and the detection target object 42 are arranged at a plurality of positions in a predetermined range 44 within the imaging range of the camera 11 so that the normal direction of the detection target surface of the detection target object 42 is parallel to the camera 11. Thus, one dictionary is generated in advance using images obtained by capturing the detection object 42 in advance at each of a plurality of positions.
The predetermined range 44 is a range to be detected by the detection unit 23. At a position greatly deviated from the center of the captured image, the shape changes greatly compared to the center. For this reason, the predetermined range 44 is preferably a range in which the shape change is not too large. For example, from the visual axis 40 parallel to the traveling direction of the host vehicle 41 to the outside of the host vehicle 41 from 30 degrees to 60 degrees. The range is up to about 50 degrees (for example, up to 45 degrees). Further, when the detection target 42 is a truck or a bus, the detection target plane can be regarded as more parallel. For this reason, the predetermined range 44 may be expanded from the visual axis 40 parallel to the traveling direction of the host vehicle 41 to about 85 degrees toward the outside of the host vehicle 41.
For example, when it is set as the range which exceeds 45 degree | times toward the outer side of the own vehicle 41 from the visual axis 40, the side surface of the detection target object 42 will also be visible. For this reason, when it is set as the range exceeding 45 degrees, the side surface of the detection target object 42 is set as the detection target surface, and the visual axis 40 is made parallel to the normal direction of the side surface (direction perpendicular to the traveling direction of the host vehicle 41). A dictionary may be generated separately.
At this time, the detection target surface may be a small region such as a part of the front surface of the vehicle or a tire. If the detection target surface cut out in the dictionary when the line-of-sight axis 40 is parallel to the normal direction of the side surface is a small region, the effect of decreasing the resolution becomes farther away and the side surface of the detection target 42 becomes farther away (is not a perfect plane). And) the influence of distortion can be reduced.
In FIG. 3, it is assumed that another vehicle approaching from the own vehicle 41 is detected by traveling in the lane next to the lane in which the own vehicle 41 travels, and from the camera 11 to the predetermined distance from the own line of sight 40. An example in which the range up to 45 degrees toward the outside of the vehicle 41 is set to a predetermined range 44 when the range of the predetermined distance from the visual axis 40 toward the outside of the host vehicle is set to a predetermined range 44 at a distance farther from the camera 11 than the predetermined distance. It was.
(Pyramid image generation)
Subsequently, a pyramid image generated by the processed image generation unit 22 according to the present embodiment will be described.
FIG. 4 is an explanatory diagram illustrating an example of the visual axis 40 and the projection surface 43 of each camera 11 when the cameras 11 are provided on the left and right and the rear, and the front surface of another vehicle running in parallel with the host vehicle 41 is detected. When detecting the front surface of another vehicle running parallel to the host vehicle 41, the processed image generating unit 22 has a line of sight axis 40 parallel to the traveling direction of the host vehicle 41 and directed backward based on the captured image of the camera 11. A processed image that is perspective-projected at 43 is generated.
Alternatively, the cameras 11 may be provided on the left and right and the front, and the rear surface of another vehicle running in parallel with the host vehicle 41 may be detected. When detecting the rear surface of another vehicle running in parallel with the host vehicle 41, the processed image generating unit 22 has a projection plane having a line of sight axis 40 that is parallel to the traveling direction of the host vehicle 41 and forwards based on the captured image of the camera 11. A processed image that is perspective-projected at 43 is generated.
Alternatively, the cameras 11 may be provided on the left, right, front, and rear to detect the side of another vehicle running in parallel with the host vehicle 41. When detecting the side surface of another vehicle running in parallel with the host vehicle 41, the processed image generation unit 22 sees through the projection plane 43 having a visual axis 40 perpendicular to the traveling direction of the host vehicle 41 based on the captured image of the camera 11. A projected processed image is generated.
When detecting the front surface, side surface, and rear surface of another vehicle, an individual dictionary corresponding to each line-of-sight axis 40 is used. These dictionaries are generated in advance by the dictionary generation unit 21. Of course, it is also possible to prepare dictionaries corresponding to all and to detect all detection target surfaces (front, rear and side surfaces of other vehicles).
FIG. 5 is an explanatory diagram illustrating an example of the pyramid image 50 generated by the processed image generation unit 22. In FIG. 5, the numbers 0 to 16 are shown in order from the most reduced image (image having the lowest resolution). FIG. 5 shows an example in which a plurality of processed images are classified into a short-distance image, a middle-distance image, and a long-distance image according to the enlargement / reduction ratio. Also, FIG. 5 shows that the near-distance image (0-8) has a magnification of 1.3 times each other, and the intermediate-distance image (9-12) has a magnification of 1.19 times each other, a long-distance image ( An example is shown in which 13-16) are 1.19 times larger than each other.
In the present embodiment, the dictionary generated by the dictionary generation unit 21 corresponds to an image having a predetermined size (for example, horizontal × vertical is 24 × 12 pixels) according to the detection target 42. In this case, the detection unit 23 scans a frame of a predetermined size (for example, 24 × 12 pixels) on the processed image. On the other hand, the size of the detection object 42 on the processed image varies depending on the distance from the own vehicle 41. For example, when the detection target 42 is far from the host vehicle 41, the detection target 42 appears smaller on the processed image than when it is close.
For this reason, the processed image generation unit 22 generates an image group (pyramid image) 50 including a plurality of processed images having different distances of the projection plane 43 set from the own vehicle 41. At this time, the processed image generation unit 22 has the same visual axis 40 as the visual axis 40 used for generating the dictionary so that each processed image has the visual axis 40 parallel to the normal direction of the detection target surface. Thus, each processed image is generated.
The distance from the own vehicle 41 to the detection object 42 when the size of the detection object 42 on the processed image matches the size of the frame can be measured in advance. For example, if the vertical width of the frame is h, the focal length of the image is f, and the actual vertical width is S, the distance D to the detection target can be written as follows.
D = f · S / h
Therefore, each processed image can be associated with the distance from the own vehicle 41 to the detection target 42 in advance. The smaller the processed image, the shorter the distance from the associated vehicle 41. Since the surface of the processed image here is a plane, the distance in each processed image is a distance with respect to the visual axis direction. The surface of the processed image may be cylindrical or spherical. For example, when the surface is a cylinder, the focal length is a circle (two-dimensional) direction, and the distance to the detection target 42 is a circle (two-dimensional) direction. For example, when the surface is a sphere, the focal length is the sphere (three-dimensional) direction, and the distance to the detection target 42 is the sphere (three-dimensional) direction.
Each processed image is only required to be associated with a different distance from the own vehicle 41, and may be generated by enlarging or reducing the captured image of the camera 11 at a plurality of magnifications. It may be generated every distance to the object 42 (for example, 2 m).
The processed image generation unit 22 sets a projection plane 43 for each predetermined distance from the own vehicle 41, and generates each processed image based on the captured image using each projection plane 43.
When each projection plane 43 has the same line-of-sight axis 40, the detection unit 23 can apply one dictionary to all processed images constituting the pyramid image 50. When the pyramid image 50 is generated, the detection unit 23 scans a frame of a predetermined size on each processed image, and obtains a detection frame obtained using a HOG dictionary and an estimated distance based on the HOG feature amount.
(Scanning frame)
FIG. 6 is a diagram for explaining a scanning frame (region to be a HOG process (detection target surface)) when the detection target 42 is an automobile.
The HOG processing unit 31 scans a scanning frame having a size corresponding to the detection target surface (HOG processing region) of the detection target 42 on the processing image, and obtains the HOG feature amount using the HOG dictionary for each scanning position. Find the likelihood. Based on the likelihood, the detection frame is obtained by the HOG result selection unit 33.
The detection target surface is preferably a region that can be regarded as a flat surface to some extent even if it is not a complete flat surface. For this reason, when the detection target 42 is an automobile and the line-of-sight axis 40 is parallel to the traveling direction of the own vehicle 41, the detection unit 23 may set the detection area as a detection target surface in front of the automobile (another vehicle). . Since the windshield part is located behind the light 41 from the vehicle 41, if the windshield part is located beside the vehicle 41 in the vicinity, the lateral position is shifted between the windshield part and the light and a processed image is created. It is because it ends.
In addition, when only the position far from the own vehicle 41 is set to the predetermined range 44, the depth of the windshield portion and the light is relatively the same, so the entire vehicle including the windshield is covered with the detection target surface (HOG Processing area).
Further, when the visual axis 40 is perpendicular to the traveling direction of the host vehicle 41, the side surface of the other vehicle may be set as the detection target surface. Note that the detection target surface may be a small region such as a part of the front surface of the vehicle or a tire. If the detection target surface is a small region, it is possible to reduce the influence that the resolution decreases as the distance increases, and the influence that the side surface of the detection object 42 becomes distorted as the distance increases.
There is only one dictionary, and the detection object 42 shown on the image captured by the wide-angle camera 11 is shown at a position away from the center of the image and compared to the case where it is shown at the center of the image. This also corresponds to the case where the shape is distorted. For this reason, even if the shape of the detection target 42 is distorted on an image captured over a wide range using the wide-angle camera 11, the detection unit 23 is flexible and stable while suppressing erroneous detection with one dictionary. Thus, the detection object 42 can be detected.
Further, the image processing apparatus 10 can detect the detection object 42 located in a wide range with one dictionary. For this reason, compared with the case where a plurality of dictionaries are used, the required storage capacity can be greatly reduced, and the load on the detection process can be reduced and the processing time can be reduced. Further, when the other vehicle is close to the own vehicle 41, the image processing apparatus 10 sets the visual axis 40 in a direction perpendicular to the traveling direction of the own vehicle 41 and a predetermined range 44 along the traveling direction of the own vehicle 41. A wide range can be used, and the detection target surface can be a side surface of another vehicle or a detection target. For this reason, especially when the camera 11 is provided in the vicinity of the side mirror, it is possible to monitor other vehicles overtaking the own vehicle 41 up to the vicinity of the own vehicle 41.
(Setting the scanning frame according to the detection object)
When the detection object 42 is an automobile, the detection frame having a high likelihood as the detection object 42 in the processed image by the HOG process is used, and the vertical direction of the detection frame obtained by normalizing the size and position of the frame in the normalized image is used as a reference. The width Hh and the horizontal width Hw can be associated with the actual vertical width Oh and horizontal width Ow of the detection target surface (for example, the vertical width Oh 0.5 m and the horizontal width Ow 1.7 m for the front light). For this reason, it is possible to obtain the distance of the detection object 42 from the vehicle 41 based on the information on the position and size of the detection frame on the normalized image.
On the other hand, when there are various sizes of the detection object 42, the processing is easy if the ground position is limited. On the processed image, the position of the ground is higher as the ground is farther. If considered for each ground, scanning is only in the horizontal direction within one processed image. Therefore, the detection unit 23 classifies the size of the detection object 42 into a plurality of sizes, provides scanning frames having different sizes for each classification size, and sets the classification size on each of the plurality of processed images constituting the pyramid image 50. Each of the scanning frames is scanned.
Examples of cases where the detection object 42 has various sizes include a case where the detection object 42 is a person. The vertical width (height) of a person has a large width depending on age and the like.
FIG. 7A is an explanatory diagram illustrating an example of a normalized image in which the focal length is f, and FIG. 7B is a diagram in which the detection target 42 is a person and the vertical width Oh of the scanning frame is classified into three types. It is explanatory drawing which shows an example of the mode in the case of doing.
As shown in FIG. 7A, if the ground position is limited, a scanning frame can be set for the detection target 42 for each certain size. In this case, the calculation may be performed assuming that each detection frame obtained by scanning with each scanning frame has the same vertical width Oh.
When the detection object 42 is a person, for example, as shown in FIG. 7B, the actual vertical width Oh of the detection object 42 is Oh1 = 0.75 m (0.45 m to 1.05 m) and Oh2 = 1.25 m. (0.95 m to 1.55 m) and Oh3 = 1.75 m (1.45 m to 2.05 m). In the case of a divided person having an intermediate height, detection is performed in an overlapping manner. In that case, it is preferable to employ a person with more detection frames or a higher likelihood.
FIG. 8A is an explanatory diagram illustrating an example of a focal position image obtained by detecting a person standing on the ground, and FIG. 8B illustrates a method for calculating the lower end Hyb of the detection frame in the example illustrated in FIG. It is a figure for doing.
The height of the camera 11 from the ground is Ch, the actual vertical width of the detection object 42 is Ohi (where i = 1, 2, 3), and the vertical width of the detection frame on the focal position image is Hhi (where i = 1). 2, 3) Let the coordinates of the center of the visual axis on the focal position image be (px, py, f). In this case, as shown in FIG. 8A, when the lower side of the detection frame is the ground position, the position of the lower end Hbyi (where i = 1, 2, 3) of the detection frame on the focal position image is as follows. You can write
Ohi: Hhi = Ch: (Hybi-py)
Hybi = (Hhi ・ Ch / Ohi) + py
Thus, by setting the actual vertical width as a range (for example, in the above example, Oh1 is in the range of 0.45 m to 1.05 m), the vertical position has a margin, and only that region is scanned. If it is a frame. As a result, the detection frame is only close to each classified vertical width (in the above example, the difference from the vertical width is within 0.3 m). Accordingly, the detection object 42 can be calculated by assuming that the detection frame 42 has the same vertical width Oh for each detection frame obtained by scanning with each scanning frame.
FIG. 9 is an explanatory diagram illustrating an example of a focus position image in which a person who has floated from the ground by a predetermined height is detected.
As shown in FIG. 9, when detecting an object floating by a predetermined height ΔOhi (where i = 1, 2, 3), the lower end Hybi of the detection frame on the focal position image (where i = 1, 2, 3)
Hybi = [Hhi ・ (Ch -ΔOhi) / Ohi] + py
And processing can be performed in the same manner as when the lower side of the detection frame is the ground position.
(Outline of TTC calculation procedure)
Subsequently, a method of calculating TTC (time to collision) by the TTC calculation unit 24 according to the present embodiment will be described.
FIG. 10 is a subroutine flowchart showing an example of the procedure of the TTC calculation process executed by the TTC calculation unit 24 in step S7 of FIG.
In step S <b> 11, the TTC calculation unit 24 acquires the detection frame output by the HOG result selection unit 33.
Next, in step S12, the TTC calculation unit 24 calculates the estimated distance of the detection object 42 from the detection frame.
Next, in step S13, the TTC calculation unit 24 selects a plurality of effective distances based on the history of the estimated distance and the pyramid image distance, and obtains the selected effective distance and the estimated distance acquisition time corresponding to each effective distance. The data are stored in the storage unit 13 in association with each other.
Next, in step S14, the TTC calculation unit 24 calculates the velocity of the detection target object 42 using a plurality of effective distances.
In step S15, the TTC calculation unit 24 obtains a time TTC until the detection target 42 collides with the host vehicle 41 using the calculated speed, and proceeds to step S8 in FIG. Note that TTC may be a time until the detection target 42 reaches a position of a predetermined distance Dc from the own vehicle 41.
Through the above procedure, an accurate TTC can be obtained by using the history of estimated distance and the pyramid image distance.
(Effective distance selection method)
Next, an effective distance selection method will be described. In the following description, the method for obtaining the distance in the z direction in the example shown in FIG. 3 will be described, but the same can be obtained for the x direction.
FIG. 11 is a diagram for explaining a first effective distance selection method.
The first selection method of the effective distance is when the number of detected frames is one or when one result with the highest likelihood is selected (when the distance of the detection frames matches one of the pyramid image distances). In addition, the estimated distance is smoothed based on the history of the estimated distance while using the pyramid image distance.
Consider the case where the pyramid image distance is Dzp0, Dzp1, Dzp2, Dzp3,. At this time, for each distance Dzp0, Dzp1, Dzp2, Dzp3,..., A predetermined width is set by a distance r · Δd of a predetermined ratio r (for example, 0.2) of the distance Δd between adjacent processed images. The given range is set as the belonging range of each processed image. In other words, each processed image belongs to the range from the near boundary 61 to the far boundary 62 when viewed from the own vehicle 41.
In the first effective distance selection method, the TTC calculator 24 smoothes the estimated distance by obtaining a corrected estimated distance obtained by correcting the current estimated distance α using the following correction formula corresponding to the previous estimated distance β. To do.
Correction estimated distance = β + (α−β) · c
Here, the proportionality constant c (0 <c ≦ 1) may be determined according to the maximum relative speed of the detection object 42. For example, the nearest pyramid image distance equal to or greater than the current estimated distance is Dzpi, and the next nearest pyramid image distance is Dzpj. Assuming that the maximum relative speed (the distance traveled during one process) is vmax, the minimum time (number of processes) tmin that passes between two pyramids is
tmin = (Dpzi-Dpzj) / vmax
The proportionality constant c can be written as follows by multiplying the proportionality constant c0.
c = c0 / tmin
Here, the proportionality constant c0 may be 1, for example, and the proportionality constant c may be limited to a maximum of 0.2 and a minimum of 0.05, for example. Alternatively, a corrected estimated distance obtained by limiting the movement amount from the previous estimated distance β at the maximum relative speed may be used. Or you may reduce the frequency | count of a process per time, so that the adjacent pyramid image distance is long.
The TTC calculation unit 24 has the estimated distance after correction (see the straight line in FIG. 11) within the affiliation range of the processed image or the previous estimated distance is opposite to the affiliation range of the processed image, When the stored effective distance does not belong to this processed image, the estimated distance after the correction or the estimated distance of the current correction is selected as the effective distance and is stored in the storage unit 13 together with the acquisition time of the estimated distance of the current time. .
For example, at time t = t2 in FIG. 11, the current estimated distance after correction has entered the far boundary 62 of Dzp2, and is in the affiliation range of Dzp2. In addition, although the previous estimated distance at t = t2 is Dzp2 (see x on the left of t = t2), the estimated distance after the previous correction does not belong to any affiliation range of any processed image. Therefore, the TTC calculation unit 24 selects the current estimated distance Dzp2 + γ or the current estimated distance Dzp2 as an effective distance, and stores it in the storage unit 13 together with the current estimated distance acquisition time t2.
In addition, when straddling a plurality of processed images at a time, the distance of the straddled processed images is ignored, and the processed image to which the estimated distance after the correction this time belongs or the closest processed image is within the belonging range. And the estimated distance may be the effective distance.
According to the first selection method, the position and speed of the detection object 42 can be obtained more stably than when the distance of the processed image, which is a jump value, is used in a single shot.
FIG. 12 is a diagram for explaining a second method for selecting an effective distance.
The second method for selecting the effective distance is when the number of detected frames is one or when the result with the highest likelihood is selected (when the distance of the detection frame matches one of the pyramid image distances). In addition, instead of smoothing the estimated distance, the effective distance is determined when the estimated distance is continuously within a affiliation range of one processed image for a predetermined number of times or outside the affiliation range opposite to the effective distance stored most recently. It is.
For example, if the number of continuous times is 3, the TTC calculation unit 24 causes the storage unit 13 to store the estimated distance when the image belongs to 3 consecutive times within the affiliation range of a certain processed image together with the acquisition time (see FIG. 12). .
Also according to the second method, the position and speed of the detection object 42 can be obtained more stably than in the case where the distance of the processed image, which is a jump value, is used only once.
FIG. 13A is an explanatory diagram showing an example of a focal position image for explaining a third method for selecting an effective distance, and FIG. 13B shows a detection target object from the own vehicle 41 in the example shown in FIG. It is a figure for demonstrating the calculation method of distance Dz to 42. FIG.
The third method for selecting the effective distance is a simple average of a plurality of effective results, a weighted average by likelihood, or the like (when the distance of the detection frame may not match one of the pyramid image distances). In this method, the distance Dz from the own vehicle 41 to the detection object 42 is calculated based on the detection frame sizes Hh and Hw and the actual detection object size Oh and Ow.
The focal length is f, the coordinates of the center of the visual axis on the normalized image are (px, py, f), and the vertical width, horizontal width, and center coordinates of the detection frame on the normalized image are Hh, Hw, and (Hx, Hy, f ) The actual vertical width and horizontal width of the detection object 42 are set to Oh and the horizontal width Ow. At this time, the distance Dz in the z direction and the distance Dx in the x direction from the vehicle 41 to the detection target 42 can be expressed as follows.
Dz = f · Ow / Hw
Dx = (Hx−px) · Dz / f
The distance Dz obtained by this calculation is expected to be close to the distance Dzp0 of the processed image although it depends on the speed of the detection target 42 and the resolution (distance interval) of the processed image. For this reason, when the distance Dz obtained by the calculation is within the affiliation range of the processed image (for example, Dzp2 + γ), the distance (for example, Dzp2) from the vehicle 41 associated with the processed image is used as the estimated distance this time. . Further, the estimated distance may be smoothed as in the first selection method.
In these first to third selection methods, the processing load may be reduced by thinning and processing the distances of all the processed images without setting them as processing targets.
(Details of TTC calculation procedure)
The TTC calculation unit 24 may calculate the TTC when the effective distance stored in the storage unit 13 is three or more. The TTC can be calculated if the effective distance of two points, its acquisition time, and the current time are known. However, it is preferable not to use the effective distance stored first because it is not possible to determine whether or not the effective distance stored at that time has entered the affiliation range of the processed image. For this reason, three or more stored values are required.
FIG. 14 is a diagram for explaining a TTC calculation method based on the effective distance. FIG. 14 shows an example in which the first effective distance selection method shown in FIG. 11 is used, and the near boundary 61 and the far boundary 62 are omitted in order to avoid complexity.
First, the velocity v1 of the detection target 42 is obtained using the two effective distances stored most recently and the acquisition time thereof.
For example, consider the case where the current time is t = t, the two effective distances stored most recently and their acquisition times are (Dzp1, t1) and (Dzp2, t2), respectively. In this case, the speed v1 can be expressed as follows.
v1 = (Dzp2-Dzp1) / (t1-t2)
At this time, when it is known that the detection object 42 does not move steeply in a short time, if more than two effective distances are stored within a short time (for example, 1 second), The speed v1 may be calculated by the latest and the past calculation at an effective distance in time, the average or the least square method.
Using this speed v1, the time TTC until the detection object 42 reaches the position of the predetermined distance Dc from the own vehicle 41 can be written as follows.
TTC = (Dzp1-Dc) / v1- (t-t1)
Here, TTC represents the time when the detection target 42 reaches the position of the distance Dc from the own vehicle 41 when it is assumed that the detection target 42 moves at the speed v1.
FIG. 15 is a diagram for explaining a method of recalculating TTC when the relative speed between the detection object 42 and the host vehicle 41 decreases.
As shown in FIG. 15, after reaching the affiliation range of a certain processed image (Dzp1 in the example shown in FIG. 15), the relative speed between the detection target 42 and the own vehicle 41 may become almost zero. In this case, the detection target object 42 appears to stagnate within the affiliation range of the processed image. For this reason, the actual TTC is longer than the TTC calculated based on the speed v1 calculated by the method shown in FIG.
For example, in the example shown in FIG. 14 and FIG. 15, the detection object 42 still does not reach Dzp0 even if the current time t = t is the time that should have reached Dzp0 predicted based on the speed v1. This condition can be expressed as follows.
t-t1> (Dzp1-Dzp0) / v1
Therefore, it is assumed that the distance of the processed image (Dzp1 in the examples shown in FIGS. 14 and 15) to which the detection object 42 belongs at the time of calculating v1 has just reached the distance of the next processed image (Dzp0 in the example). Then, the velocity is recalculated using the latest effective distance and its acquisition time (Dzp1, t1). Assuming that the recalculation speed is v, it is assumed that Dzp0 has been reached at t = t at present, so v can be expressed as follows.
v = (Dzp1-Dzp0) / (t-t1)
Using this recalculation rate v, the TTC can be recalculated as follows.
TTC '= (Dzp0-Dc) / v
Here, TTC ′ represents the TTC recalculated by the above equation at the recalculation speed v. In addition, considering that it is stagnant due to an error, it is assumed that the TTC 'is not increased but is updated as the same value or approached as it is because it is stagnant for a predetermined period from the time it should arrive You may make it smaller.
In addition, when the cause that seems to be stagnant in the affiliation range of the processed image is some trouble such as erroneous detection, the detection target 42 may actually approach the own vehicle 41. In this case, when it is known that the detection target 42 does not move steeply in a short time, the velocity v may be calculated assuming that the relative acceleration does not become larger than a, for example. The range of the speed v predicted based on the speed v1 obtained from the effective distances of the two nearest points can be written as follows by predicting the amount of change in the speed v1 from t = t1 by the relative acceleration.
v1-a ・ (t-t1) ≦ v ≦ v1 + a ・ (t-t1)
Therefore, the TTC may be calculated by setting the speed v within this range. Further, since the relative acceleration is the relative acceleration between the camera 11 (the own vehicle 41) and the detection target 42, when the image processing apparatus 10 includes the vehicle information acquisition unit 14, the acceleration of the camera 11 (the own vehicle 41) The speed may be acquired from the vehicle information acquisition unit 14 and offset to determine the acceleration range of the detection target 42.
Further, the current distance Dz of the detection target 42 may be recalculated using the recalculated velocity v and the recalculated TTC ′ in the example shown in FIG.
Dz ′ = Dc + v · TTC ′
Here, Dz ′ represents the recalculated current distance.
In the above description, the processing related to the distance Dz along the z direction has been described. However, the distance Dx along the x direction may be processed in the same manner, or only the estimated distance may be smoothed.
Further, the position of the detection frame on the processed image may be corrected using the recalculated current distance Dz ′.
FIG. 16A is an explanatory diagram showing an example of a focal position image when the lower end of the detection frame is only Dy above the ground, and FIG. 16B shows the recalculation distance Dz ′ in the example shown in FIG. It is a figure for demonstrating the method to correct the position of the detection frame on a process image using it.
For example, let Dy be the height from the lower and upper ends of the detection frame and the ground between them. When the ground is inside the detection frame and the lower end of the frame is buried below the ground, the height Dy from the ground is negative. In addition, when Dy is not the height from the ground, Dy may be a fixed value. However, since it is assumed that the height Dy changes depending on the type and size of the detection target 42, the past The height from the ground may be calculated from the size of the detection frame at the time of detection, the estimated distance and the lower side position, and the height Dy may be calculated from the average or the result of the least square method.
For example, if the height of the camera 11 from the ground is Ch and the position of the lower side of the detection frame is Hyb, Dy can be expressed by the following equation.
Dz: f = (Ch-Dy): (Hyb-py)
Dy = Ch- (Hyb-py) .Dz.f
Further, in the case of using an expression that does not use the height Ch of the camera 11, the following expression using the position Dy ′ from the horizontal line may be used instead of Dy.
Dz: f = Dy ′: (Hyb-py)
Dy ′ = (Hyb-py) · Dz · f
As described above, the image processing apparatus 10 according to the present embodiment obtains the estimated distance from the result of the HOG process using the processed image (composition image of the pyramid image) associated with the jump distance, and the history of the estimated distance. And TTC can be calculated using the pyramid image distance. For this reason, the image processing apparatus 10 detects the detection target 42 included in the processed image using the pyramid image generated from the image acquired by the camera 11, and detects the TTC (collision of the detected detection target 42. Time) can be accurately predicted.
For example, the captured image used by the image processing apparatus 10 does not have to be a captured image of the camera 11 provided in the vehicle, but is an image captured by a general peripheral monitoring camera such as a camera installed on a streetlight. May be.
DESCRIPTION OF SYMBOLS 10 Image processing apparatus 11 Camera 13 Memory | storage part 18 Speaker 19 Display apparatus 22 Processed image generation part 23 Detection part 24 TTC calculation part 25 Warning part 41 Own vehicle 42 Detection target object
Each time for acquiring images from a camera provided in a vehicle, and generate unit that generates a pyramid images obtained images as a reference image,
Wherein the pyramid images are generated for each of all the images forming the pyramid images, while moving the model part to be compared of the detection object in an image by scanning the image, the of the partial A scanning unit that acquires a position of the part in the image when the likelihood for the model is equal to or greater than a threshold;
Of the images forming the pyramid image group, the image likelihood for the model exists portion is equal to or greater than the threshold, obtains a ratio to the reference image, based on the magnification, and the detection object and the vehicle An estimation unit for obtaining a corrected estimated distance by correcting the obtained estimated distance using the previous estimated distance obtained when an image was acquired from the camera last time,
When the predetermined condition is satisfied, a selection unit that stores the correction estimated distance of this time in the storage unit in association with the acquisition time of acquiring the image from the camera this time,
Based on the plurality of corrected estimated distances stored in the storage unit and the acquisition time associated with each of the plurality of corrected estimated distances, a time until the own vehicle and the detection object collide is calculated. A calculation unit to be issued;
For each image constituting the pyramid image group, a range in which a predetermined width is given to the magnification relative to the reference image is set as the belonging range of the image, and (1) a magnification corresponding to the current correction estimated distance is one belonging range. And a magnification corresponding to the corrected estimated distance most recently stored in the storage unit does not belong to the one belonging range, or (2) a magnification corresponding to the current corrected estimated distance; In a case where a plurality of belonging ranges of the respective images are included between the magnification corresponding to the previous corrected estimated distance, the current corrected estimated distance is stored in the storage unit.
TTC measuring device.
Of the images forming the pyramid image group, the image likelihood for the model exists portion is equal to or greater than the threshold, obtains a ratio to the reference image, based on the magnification, and the detection object and the vehicle an estimation unit that estimates a distance Ru sought between,
When the estimated distance obtained by the estimating unit is the same continuously for a predetermined number of times including the estimated distance of the current time, the estimated distance of the current time is associated with the acquisition time when the image is acquired from the camera this time and stored in the storage unit. A selection section to be memorized,
To de San plurality of the estimated distance stored in the storage unit, the time until the subject vehicle and the detection target object collides based on said acquisition time associated with each of the plurality of the estimated distance A calculation unit;
A TTC measuring apparatus comprising:
A plurality of models having different sizes from each other, and scanning of all images constituting the pyramid image group is performed for each model .
The TTC measuring apparatus according to claim 1 or 2 .
The calculation unit includes at least one of an audio output and a buzzer output via a speaker of the vehicle and a warning display for a display device provided at a position where the driver of the vehicle can visually recognize the vehicle driver. Warning section that notifies the information output by
Further claims 1 equipped with to TTC measuring device according to any one of 3.
Each time for acquiring images from a camera provided in a vehicle, generate unit that generates a pyramid images obtained images as a reference image,
Wherein the pyramid images are generated for each of all the images forming the pyramid images, while moving the model part to be compared of the detection object in an image by scanning the image, the of the partial A scanning unit that acquires the position of the part in the image when the likelihood for the model is equal to or greater than a threshold;
Of the images forming the pyramid image group, the image likelihood for the model exists portion is equal to or greater than the threshold, obtains a ratio to the reference image, based on the magnification, and the detection object and the vehicle An estimation unit for obtaining an estimated distance by correcting the current estimated distance obtained by using the previous estimated distance obtained when an image was acquired from the camera last time,
A selection unit that stores the correction estimated distance of this time in the storage unit in association with the acquisition time of acquisition of the image from the camera this time when the predetermined condition is satisfied; and
Based on the plurality of corrected estimated distances stored in the storage unit and the acquisition time associated with each of the plurality of corrected estimated distances, a time until the own vehicle and the detection object collide is calculated. Calculating unit to be issued,
TTC measurement program.
Of the images forming the pyramid image group, the image likelihood for the model exists portion is equal to or greater than the threshold, obtains a ratio to the reference image, based on the magnification, and the detection object and the vehicle estimator asking you to estimate distance between,
When the estimated distance obtained by the estimating unit is the same continuously for a predetermined number of times including the estimated distance of the current time, the estimated distance of the current time is associated with the acquisition time when the image is acquired from the camera this time and stored in the storage unit. Selection section to be memorized, and
To de San plurality of the estimated distance stored in the storage unit, the time until the subject vehicle and the detection target object collides based on said acquisition time associated with each of the plurality of the estimated distance Calculation part,
TTC measurement program to function as
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