Object detection device

Abstract

To provide an object detection device that can suppress false detections caused by the influence of unrelated light sources or objects by estimating how shadows change based on changes in the position of a light source and comparing this with the actual amount of change in the candidate shadow area. [Solution] To achieve the above objective, the object detection device (101) of the present invention includes a shadow candidate region extraction unit (210) that extracts shadow candidate regions (413, 414), which are candidate regions where the shadow of a three-dimensional object illuminated by a light source (403) is captured from an image captured by a camera (103) installed on a vehicle (401); a region size calculation unit (211) that determines the amount of change in the size of the shadow candidate region due to the movement or change in position of the light source; and a three-dimensional object detection unit (212) that determines whether or not a three-dimensional object forming the shadow candidate region exists based on the amount of change in the size of the shadow candidate region.

Description

Technical Field

[0001] The present invention relates to an object detection device.

Background Art

[0002] In an object detection system using an in-vehicle camera, an object detection device that detects relatively low three-dimensional objects such as fallen objects and tires on the road is known. As a means for detecting an arbitrary low three-dimensional object, object detection by grouping three-dimensional point clouds using three-dimensional point cloud measurement by a stereo camera can be mentioned. However, the low three-dimensional object has a small difference from the three-dimensional points measured on the road surface, making it difficult to separate from the measurement error, and there is a problem of misidentifying a plane such as road paint as a low three-dimensional object.

[0003] As a detection technique for low three-dimensional objects using information other than three-dimensional measurement information, there is Japanese Patent Application Laid-Open No. 2014-72681 (Patent Document 1). In this publication, it is described as a problem that "since an object is detected based on a shadow generated by light irradiation from one direction, there may be a case where almost no shadow is generated depending on the direction in which the object is irradiated with light." And as a solution, it is described that "in a first aspect of the present invention, there is provided an acquisition unit that acquires a captured image from a capturing unit that captures an object and generates a captured image, a control unit that controls an irradiation unit that irradiates the object with a plurality of lights from different directions, a recognition unit that recognizes an image of a shadow generated by the irradiation unit irradiating the object with a plurality of lights in the captured image, and a detection unit that detects an object based at least on the shadow recognized by the recognition unit."

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In object detection using light sources and shadows, light sources were placed at known locations, and images were captured while switching between the installed light sources. The areas where changes occurred were identified as shadows, and objects were detected.

[0006] However, when performing object detection in diverse scenes, such as with in-vehicle cameras, there is a possibility that light sources outside the system, such as the lights of other vehicles, or shadows cast by objects other than the target of detection, may be captured. In other words, there was a problem that false detections could occur due to external disturbances.

[0007] The objective of the present invention is to provide an object detection device that suppresses false detections caused by the influence of unrelated light sources or objects. [Means for solving the problem]

[0008] To achieve the above objective, the present invention includes: a shadow candidate region extraction unit that extracts shadow candidates, which are candidate regions in which the shadow of a three-dimensional object illuminated by a light source is captured, from an image captured by a camera installed on a vehicle; a region size calculation unit that determines the amount of change in the size of the shadow candidate region due to the movement or change in position of the light source; and a three-dimensional object detection unit that determines whether or not a three-dimensional object exists that forms the shadow candidate region based on the amount of change in the size of the shadow candidate region. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide an object detection device that can suppress false detections caused by the influence of unrelated light sources or objects.

[0010] Further features related to the present invention will become apparent from the description herein and the accompanying drawings. Problems, configurations, and effects not described above will be revealed by the following description of embodiments. [Brief explanation of the drawing]

[0011] [Figure 1] Hardware configuration diagram of an object detection device. [Figure 2]A functional block diagram illustrating the internal functions of the image processing unit. [Figure 3] A functional block diagram illustrating the internal functions of the domain size calculation unit. [Figure 4] A diagram illustrating a specific example of calculation processing performed by the domain size calculation unit. [Figure 5] A diagram showing an example of changes in the potential shadow area on the road surface. [Figure 6] A functional block diagram illustrating the internal functions of the region size calculation unit in Example 2. [Figure 7] An explanatory diagram showing how to use the headlights of an oncoming vehicle as a light source. [Figure 8] A diagram showing an example of changes in the potential shadow area on the road surface. [Figure 9] A functional block diagram illustrating the internal functions of the 3D object detection unit in Example 2. [Figure 10] A diagram illustrating an example of selection criteria. [Figure 11] This diagram illustrates an example of a combination of light source and shadow candidate region determined by the combination determination unit. [Modes for carrying out the invention]

[0012] Embodiments of the present invention will be described below with reference to the drawings.

[0013] [Example 1] Figure 1 is a hardware configuration diagram of the object detection device. The object detection device 101 in this embodiment is mounted on the vehicle and detects objects present on the road surface in front of the vehicle, particularly low-height three-dimensional objects such as tires. The vehicle is equipped with a camera system 102 that captures images of the area in front of it. The camera system 102 has, for example, one or more on-board cameras 103 that can capture images of the area in front of the vehicle. The cameras 103 are composed of, for example, a stereo camera or a monocular camera.

[0014] The object detection device 101 is connected to the camera system 102 and acquires an image captured by the camera system 102. Note that the object detection device 101 is not limited to a configuration provided separately from the camera system 102 as in this embodiment, and may be integrally incorporated into the camera system 102, for example.

[0015] The object detection device 101 is constituted by, for example, an electronic control unit ECU, and includes components such as a memory 104 such as a RAM and a ROM, a CPU 105, and an external output unit 107. These components are communicably connected via a communication line 108. The CPU 105 executes the arithmetic processing of the image processing unit 201 described below according to the instructions of the program stored in the memory 104. The object detection device 101 realizes each function of the following image processing unit 201 by executing the program stored in the memory 104 with the CPU 105.

[0016] In the image processing unit 201, a three-dimensional object is detected by various processes using the image captured by the camera 103. The specific object detection process will be described later. The object detection result is transmitted from the object detection device 101 to the outside via the external output unit 107, and is used by other functions of the host vehicle such as preventive safety control, for example. These functions may be incorporated as part of the image processing device.

[0017] Hereinafter, the components of the image processing unit will be described. FIG. 2 is a functional block diagram for explaining the internal functions of the image processing unit. As shown in FIG. 2, the image processing unit 201 has a shadow candidate region extraction unit 210, a region size calculation unit 211, and a three-dimensional object detection unit 212 as internal functions.

[0018] The shadow candidate region extraction unit 210 performs a process of extracting, as a shadow candidate region, a region in the image where the shadow of a three-dimensional object is estimated to be imaged. The shadow candidate region extraction unit 210 extracts, for example, a region having a lower luminance value than the periphery on the image as the shadow candidate region.

[0019] In this embodiment, since it is desirable that the shadows of three-dimensional objects be projected onto the road surface, a process may be performed to set the road surface region as a processing region from the image using a method such as semantic segmentation before brightness evaluation, and then extract candidate shadow regions from within that set processing region. However, it is difficult to distinguish between the shadows of three-dimensional objects and stains or black paint on the road surface from image texture alone. At this point, it is also acceptable to include false detection results other than shadows in the calculation of candidates.

[0020] Figure 3 is a functional block diagram illustrating the internal functions of the domain size calculation unit, and Figure 4 is a diagram illustrating the content of the calculation process performed by the domain size calculation unit.

[0021] The region size calculation unit 211 estimates what changes will occur in the actual shadow when the light source is set to two different points, assuming that there is a shadow generated when a three-dimensional object is illuminated by a light source. As shown in Figure 3, the region size calculation unit 211 includes a light source movement estimation unit 301 and a shadow region change estimation unit 302 as internal functions.

[0022] The light source movement estimation unit 301 estimates the behavior of the vehicle 401 using, for example, CAN information such as vehicle speed and yaw rate. When the headlights 403 of the vehicle 401 are used as the light source, the amount of light source movement can be estimated by using vehicle operation information such as the amount of vehicle movement and vehicle speed of the vehicle 401 over a certain period of time.

[0023] The shadow area change estimation unit 302 estimates the change in the shadow candidate area based on the obtained light source movement amount. The change in the shadow candidate area is the change in the size of the shadow candidate area extracted by the shadow candidate area extraction unit 210, and can be determined based on the change in length and direction of the shadow candidate area. For example, as shown in Figures 4(1) and (2), consider the case where the vehicle 401 travels in a straight line from point A to point B on the road surface 411 for a distance of 421, and the headlight 403 generates shadows of the three-dimensional object 412 at points A and B, respectively. In this case, as shown in Figure 4(2), the lengths s and t of the shadow candidate areas 413 and 414 are given by the following relationship (1) using the distance Z to the three-dimensional object 412 and the movement amount d of the vehicle 401. TIFF2026123623000002.tif15170

[0024] In addition, the direction in which shadows extend (di, dj) on the image can be expressed on the image as follows, using equations (2) and (3), where (X, Z) is the horizontal position and depth of the three-dimensional object 412, and (X_light, Z_light) is the horizontal position and depth of the headlight. TIFF2026123623000003.tif17170TIFF2026123623000004.tif17170In the above formula, H is the camera installation height, f is the camera focal length, and wi and wj are the camera pixel pitch.

[0025] Here, the amount of light source movement can also be determined by estimating the distance from the vehicle to the candidate shadow area based on the image. For example, one method is to estimate the distance to the object by assuming that the lower end of the detected object is in contact with the road surface, or to measure the distance using the principle of triangulation with a stereo camera system using two or more cameras. Using this information, the amount of light source movement can be estimated, and from the estimated amount of light source movement, the change in the size of the candidate shadow area can be determined.

[0026] Furthermore, even without the vehicle itself moving, it is possible to capture an image of an object illuminated by two light sources with known movement amounts by sequentially illuminating light sources located at different positions. Possible methods for utilizing light sources at different positions include the left and right headlights of the vehicle 401, or external light sources such as streetlights in conjunction with infrastructure equipment, which can be used as controllable light sources with known grounding positions. In other words, if a vehicle or infrastructure has multiple light sources whose relative positions are known and which light up sequentially in synchronization with camera imaging, the region size calculation unit 211 can determine the amount of light source movement based on the position of each light source that lit up during imaging, and calculate the amount of change in the size of the candidate shadow region. For example, this could be used when a vehicle has a configuration that allows it to sequentially light up its left and right headlights, or when it has multiple infrastructure lights such as streetlights connected to a network.

[0027] Returning to the explanation of Figure 2, the 3D object detection unit 212 compares the actual change in region size calculated from the length t of the candidate shadow region 414 extracted by the candidate shadow region extraction unit 210 with the estimated values ​​of the direction θ and length t of the shadow, which are calculated by the region size calculation unit 211 as the change in the candidate shadow region due to the movement of the light source (change in the position of the light source) using equations (2) and (3).

[0028] Then, if the change in the actual size of the candidate shadow area matches the estimated values ​​of the shadow direction θ and length t, the object is determined to be an object capable of casting a shadow and is detected as a three-dimensional object. In reality, noise has an effect, so if the difference between the change in the actual size of the candidate shadow area and the estimated values ​​of the shadow direction θ and length t is below a threshold, it is considered to match.

[0029] Figure 5 shows an example of changes in the candidate shadow area on the road surface. When the vehicle 401 is at point A, the candidate shadow region 413 extends away from the headlight 403, with the object 412 in between, due to the light 501 from the vehicle 401's headlight 403. As the vehicle 401 moves straight from point A along the Z direction and approaches the object 412, the length of the candidate shadow region 413 shortens, and at point B it becomes the candidate shadow region 414. The shadow region change estimation unit 302 estimates the change from the candidate shadow region 413 to the candidate shadow region 414 using equations (1) to (3).

[0030] According to the object detection device 101 of the embodiment described above, the device estimates how the shadow area will change based on the change in the position of the light source, compares this with the actual change in the candidate shadow area, and determines that it is a three-dimensional object if they match. This suppresses false detections caused by the influence of unrelated light sources or objects.

[0031] [Example 2] Next, Embodiment 2 of the present invention will be described. In Embodiment 2, the same reference numerals are used for components similar to those in Embodiment 1, and their detailed descriptions will be omitted.

[0032] Example 2 is a modification of Example 1 and shows an embodiment in which a light source other than the light source mounted on the vehicle (hereinafter referred to as "other light sources") is used. According to this embodiment, it is possible to utilize more light sources, and it is also possible to use light sources located in a position where the shadow candidate area extends from the three-dimensional object toward the vehicle for processing. As a result, changes in the area can be observed with greater accuracy, and detection accuracy can be improved.

[0033] Figure 6 is a functional block diagram illustrating the internal functions of the domain size calculation unit in this embodiment. The region size calculation unit 211 of this embodiment includes, in addition to the light source movement estimation unit 301 and shadow region change estimation unit 302 of Embodiment 1, a light source detection unit 601, a position measurement unit 602, and a headlight control unit 603.

[0034] The light source detection unit 601 detects other light sources from the image acquired by the camera system 102. As a detection method, for example, one method is to detect a small area with a particularly high brightness value within the image as a single light source. In typical object detection images, overexposure occurs around light sources due to saturation of brightness values, which can lead to detection failure. Therefore, it is desirable to use an image that does not experience overexposure even in bright areas through HDR processing or similar methods. Unlike the light source of the vehicle's headlights in Example 1, it is not possible to estimate the movement of other light sources from the vehicle's behavior, so the position measurement unit 602 is used to measure the position of the light sources.

[0035] The position measurement unit 602 measures the position of other light sources detected by the light source detection unit 601. If the other light source is, for example, the headlight of another vehicle, the position measurement unit 602 estimates the distance by means of estimating the distance to the object from the image coordinates of the point where the lower end of the other vehicle is installed on the road. If the other light source is a light source such as a streetlamp, where it is difficult to estimate the ground contact position, the distance can be estimated by the same process by assuming the height from the road surface. Alternatively, the distance to the other light source is measured based on multiple images captured by multiple cameras placed spaced apart on the vehicle. For example, a stereo camera system using two or more cameras is more suitable for this embodiment because it can achieve high-precision three-dimensional measurement of the other light source. Using these distance measurement results, the light source movement amount estimation unit 301 calculates the amount of movement of the other light source.

[0036] The light source movement estimation unit 301 determines the amount of change in the size of the shadow candidate region for each of the multiple other light sources, based on the positions of these multiple other light sources measured by the position measurement unit 602, if multiple other light sources are present. The three-dimensional object detection unit 212 determines whether or not a three-dimensional object exists that forms the shadow candidate region, based on the amount of change in the size of the shadow candidate region determined for at least one of the other light sources.

[0037] Figure 7 shows an example of using the headlights of an oncoming vehicle as a light source. Figure 7(1) is a schematic diagram of image 721 taken from the vehicle looking forward at a predetermined point in time, and Figure 7(2) is a schematic diagram of image 722 taken after a predetermined time has elapsed from the point in time shown in Figure 7(1).

[0038] Images 721 and 722 show the approaching oncoming vehicle 701. On the road surface 711, candidate shadow regions 713 and 714 of the three-dimensional object 712 are formed by light from another light source, the headlights 703 of the oncoming vehicle 701. As shown in Figure 7, the candidate shadow regions 713 and 714 of the three-dimensional object 712 change as the oncoming vehicle 701 moves. The change in the length of the candidate shadow region at this time can be calculated in the same way as equation (1) in Example 1.

[0039] The headlight control unit 603 adjusts the brightness of the vehicle's headlights 403 in synchronization with the imaging timing of the camera 103 if there are other light sources measured by the position measurement unit 602. When the shadow candidate area extraction unit 210 detects shadows generated by other light sources such as the headlights 703 of an oncoming vehicle 701, if the vehicle's headlights 403 strongly illuminate the shadow candidate area, there is a risk of the shadow disappearing.

[0040] Therefore, when capturing images for this process, by performing controls such as turning off the headlights 403 of the vehicle 401, changing the illumination level, or avoiding illumination in areas where shadows are generated through light distribution control, the shadow candidate area extraction unit 210 can extract shadow candidate areas with greater accuracy.

[0041] Figure 8 shows an example of changes in the candidate shadow area on the road surface. At point C, the shadow candidate region 813 extends in the direction approaching the camera 103 with the object 812 in between, due to the light 804 from the headlights 803 (another light source) of another vehicle 801. As the other vehicle 801 approaches the object 812, the length of the shadow candidate region 813 shortens, and at point D it becomes the shadow candidate region 814. The shadow region change amount estimation unit 302 shown in Figure 6 estimates the amount of change from the shadow candidate region 813 to the shadow candidate region 814 using the above-mentioned equations (1) to (3).

[0042] Next, we will explain the case where there are multiple light sources and multiple potential shadow areas. The shadow candidate region extraction unit 210 estimates multiple shadow candidate regions 414 and associates them with multiple other detected light sources (pairing) based on their shape and positional relationship. Then, the region size calculation unit 211 calculates the amount of change in the size of each shadow candidate region due to the movement of the other light sources associated with each shadow candidate region. The 3D object detection unit 212 selects shadow candidate regions and light sources appropriate for the 3D object detection process and performs 3D object detection.

[0043] Figure 9 is a functional block diagram illustrating the internal functions of the 3D object detection unit in this embodiment. The three-dimensional object detection unit 212 includes a shadow selection unit 901, a light source selection unit 902, a combination determination unit 903, and a change amount comparison unit 904.

[0044] In this embodiment, since there is a possibility of multiple light sources other than the vehicle itself being present, it is necessary to select a suitable shadow candidate area and light source for 3D object detection. The shadow selection unit 901 selects a shadow candidate area to be used in subsequent processing from among multiple shadow candidate areas. Areas with a large difference in brightness value from the surrounding area, or areas with strong edges in the shadow candidate area, are suitable because they allow for accurate estimation of the amount of change in the shadow candidate area.

[0045] The light source selection unit 902 selects a light source to be used for subsequent processing from among the multiple detected light sources. Figure 10 is a diagram illustrating an example of selection criteria. In the example shown in Figure 10, vehicle 1011 is traveling in the right lane 1002 of two lanes 1002 and 1003 on road 1001, both traveling in the same direction. A vehicle 1051 is traveling in the left lane 1003, and a relatively low-height object 1006, such as a fallen object, is lying in the right lane 1002 where vehicle 1011 is traveling. Two oncoming vehicles 1021 and 1041 are traveling in the right lane 1004 of the oncoming lane, and a vehicle 1031 is traveling in the left lane 1005 of the oncoming lane.

[0046] The light source selection unit 902 selects the appropriate headlight from an oncoming vehicle 1021, 1031, or 1041 for the vehicle 1011 and the object 1006. The position of the object 1006 may be input externally as an object detection result that may be falsely detected by combining it with an existing object detection function, or it may be near the shadow candidate area. Since it is necessary to extract the shadow candidate area and observe its size change, it is desirable that the shadow occurs as close to the vehicle 1011 as possible.

[0047] In other words, oncoming vehicle 1021, which is closer than the oncoming vehicle 1051 and the three-dimensional object 1006, is excluded. When comparing oncoming vehicles 1031 and 1041, the light from headlights and other sources diffuses with distance, so the light source closer to the three-dimensional object 1006 will produce a darker shadow on the road surface. Therefore, the light source selection unit 902 selects the headlights of oncoming vehicle 1041, which is closer to the three-dimensional object 1006, as the appropriate light source. In this embodiment, an example of selecting one light source has been described, but multiple light sources may also be selected.

[0048] The combination determination unit 903 determines which of the shadow candidate regions and light sources selected by the shadow selection unit 901 and the light source selection unit 902 should be combined to compare the amount of change. The determination criteria include the intensity of the light source, the density of the shadow, and the absolute size of the shadow candidate region assumed from the light source position information. In other words, by using the brightness values ​​of the light source and shadow candidate region on the image to select a brighter light source and a denser shadow candidate region, and by using a light source that produces a larger shadow candidate region assumed from the light source position information, the accuracy of this processing using the shadow candidate region can be improved. The combination determination unit 903 uses only pairs of light sources and shadow candidate regions in which the light source is located farther away from the shadow candidate region.

[0049] Figure 11 illustrates an example of a combination of light sources and shadow candidate regions determined by the combination determination unit. Three light sources 1101, 1111, and 1121 illuminate a three-dimensional object 1120, respectively, with light 1102, 1112, and 1122 illuminating it, forming shadow candidate regions 1103, 1113, and 1123. The three light sources 1101, 1111, and 1121 are paired with the shadow candidate regions 1103, 1113, and 1123.

[0050] Finally, the change amount comparison unit 904 compares the change amounts of the light source and the shadow candidate region combined by the combination determination unit 903, and performs 3D object detection processing in the same manner as in Example 1.

[0051] According to the embodiment described above, a more stable object detection device 101 can be provided that uses multiple light sources other than the light source mounted on the vehicle 1011.

[0052] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0053] Furthermore, each of the above configurations may be implemented either partially or entirely in hardware, or through program execution on a processor. Also, the control lines and information lines shown are those deemed necessary for illustrative purposes and do not necessarily represent all control lines and information lines in the actual product. In practice, almost all configurations can be considered interconnected. [Explanation of Symbols]

[0054] 101...Object detection device 102...Camera System 201...Image Processing Unit 210...Shadow candidate area extraction unit 211... Area size calculation unit 212...3D Object Detection Unit 301...Light source movement amount estimation section 302... Shadow area change estimation unit 601...Light source detection unit 602...Position measurement unit 603...Headlight control unit 901...Shadow Selection Department 902...Light source selection section 903... Combination Determination Section 904...Change Comparison Section

Claims

1. A shadow candidate region extraction unit extracts shadow candidate regions, which are candidate areas where the shadow of a three-dimensional object illuminated by a light source is captured from an image captured by a camera installed on the vehicle, A region size calculation unit that determines the amount of change in the size of the candidate shadow region due to the movement or change in position of the light source, A three-dimensional object detection unit determines whether or not a three-dimensional object exists that forms the shadow candidate region based on the amount of change in the size of the shadow candidate region, An object detection device characterized by having the following features.

2. An object detection device according to claim 1, An object detection device characterized in that, when the headlights of the vehicle are used as the light source, the amount of movement of the light source is estimated using the vehicle's operation information, and the amount of change in the size of the candidate shadow area is determined from the amount of movement of the light source.

3. An object detection device according to claim 1, Based on the aforementioned image, the distance from the vehicle to the candidate shadow area is estimated. An object detection device characterized in that, when the headlights of the vehicle are used as the light source, the amount of movement of the light source is estimated using the estimated change in distance, and the size of the candidate shadow area is determined from the amount of movement of the light source.

4. An object detection device according to claim 1, The area size calculation unit includes a position measurement unit for measuring the position of the light source, If multiple light sources exist, the amount of change in the size of the candidate shadow area is determined for each of the multiple light sources based on the position of the light source measured by the position measurement unit. The three-dimensional object detection unit determines whether or not a three-dimensional object exists that forms the shadow candidate region, based on the amount of change in the size of the shadow candidate region determined for at least one light source. An object detection device characterized by the following features.

5. An object detection device according to claim 4, The object detection device is characterized in that the position measuring unit measures the distance to the light source based on a plurality of images captured by a plurality of cameras positioned at intervals on the vehicle.

6. An object detection device according to claim 4, The shadow candidate region extraction unit estimates multiple shadow candidate regions and, based on their shape and positional relationship, associates them with multiple detected light sources. The object detection device is characterized in that the region size calculation unit determines the amount of change in the size of a candidate shadow region due to the movement of a light source associated with the candidate shadow region.

7. An object detection device according to claim 4, An object detection device characterized by comprising a headlight control unit that adjusts the brightness of the vehicle's headlights in synchronization with the imaging timing of the camera when a light source is detected by the position measurement unit.

8. An object detection device according to claim 4, The object detection device is characterized in that the three-dimensional object detection unit selects a combination of a candidate shadow region and a light source to be used for determination, using the shape of the candidate shadow region and the position of the light source.

9. An object detection device according to claim 1, It has multiple light sources whose relative positions are known and which light up sequentially in synchronization with the camera's imaging. The object detection device is characterized in that the region size calculation unit determines the amount of light source movement based on the position of each light source that was lit during imaging, and determines the amount of change in the size of the candidate shadow region.

10. An object detection device according to claim 9, An object detection device characterized by using the left and right headlights of a vehicle as multiple light sources.

11. An object detection device according to claim 9, An object detection device characterized by using network-connected infrastructure lighting as multiple light sources.

12. An object detection device according to claim 4, An object detection device characterized by using only pairs of light sources and shadow candidate regions where the light source is located farther away from the shadow candidate region.

Images