Patent Publication Number: US-11645838-B2

Title: Object detection system, object detection method, and program

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority from Japanese patent application No. 2020-142257, filed on Aug. 26, 2020, the disclosure of which is incorporated herein in its entirety by reference. 
     BACKGROUND 
     The present disclosure relates to an object detection system, an object detection method, and a program, and in particular, to an object detection system, an object detection method, and a program for detecting a moving object. 
     There have been techniques for detecting objects using an image capturing apparatus such as a camera. With regard to these techniques, Japanese Unexamined Patent Application Publication No. 2019-165501 discloses a tracking system that tracks one target object by a plurality of cameras. The tracking system disclosed in Japanese Unexamined Patent Application Publication No. 2019-165501 includes a first camera that captures a first video image, a second camera that captures a second video image at an angle of view substantially the same as that of the first camera, and a processor. The processor performs first tracking processing for tracking a target object on the first video image and second tracking processing for tracking a target object on the second video image. Further, the processor performs reliability calculation processing for calculating a first reliability, which is a reliability, of the target object, obtained by the first tracking processing, and a second reliability, which is a reliability, of the target object, obtained by the second tracking processing. Further, the processor compares the first reliability with the second reliability, specifies the tracking processing whose reliability is higher than that of the other tracking processing, and performs control processing for controlling, based on the result of the tracking processing that has been specified, the other tracking processing. 
     SUMMARY 
     According to the technique disclosed in Japanese Unexamined Patent Application Publication No. 2019-165501, a plurality of cameras (the first camera and the second camera) capture images with substantially the same angle of view. According to techniques of this kind, it is impossible to track one target object for positions different from one another. Therefore, it is difficult to track a moving object over a wide range. Further, while the aim of the technique disclosed in Japanese Unexamined Patent Application Publication No. 2019-165501 is to improve the accuracy of the tracking by using a plurality of cameras in one place, it is possible that a plurality of cameras cannot be installed in one place. In this case, it is difficult to improve the speed of the object recognition processing without reducing the accuracy of the recognition of the object. 
     The present disclosure provides an object detection system, an object detection method, and a program capable of tracking a moving object over a wide range, at a high processing speed, and with a high accuracy of recognition without increasing the number of image capturing apparatuses installed in one place. 
     An object detection system according to the present disclosure includes: a movement information acquisition unit configured to acquire movement information, which is information regarding a moving object included in a first image captured by a first image capturing apparatus among a plurality of image capturing apparatuses arranged in positions different from one another; a search range determination unit configured to determine a limited search range in a second image captured by a second image capturing apparatus among the plurality of image capturing apparatuses in accordance with the movement information; and an object recognition unit configured to perform recognition of the moving object for the limited search range in the second image. 
     Further, the object detection system according to the present disclosure may further include the plurality of image capturing apparatuses. 
     Further, an object detection method according to the present disclosure includes: acquiring movement information, which is information regarding a moving object included in a first image captured by a first image capturing apparatus among a plurality of image capturing apparatuses arranged in positions different from one another; determining a limited search range in a second image captured by a second image capturing apparatus among the plurality of image capturing apparatuses in accordance with the movement information; and performing recognition of the moving object for the limited search range in the second image. 
     Further, a program according to the present disclosure causes a computer to execute: acquiring movement information, which is information regarding a moving object included in a first image captured by a first image capturing apparatus among a plurality of image capturing apparatuses arranged in positions different from one another; determining a limited search range in a second image captured by a second image capturing apparatus among the plurality of image capturing apparatuses in accordance with the movement information; and performing recognition of the moving object for the limited search range in the second image. 
     In the present disclosure, according to this configuration, a moving object is detected only in a range where the moving object is highly likely to appear. Therefore, the detection speed becomes higher and the accuracy of the detection becomes higher than those in the case in which a whole image is searched. Further, since the plurality of image capturing apparatuses are installed at positions different from one another, a moving object can be detected over a wide range. Therefore, the object detection system according to the present disclosure enables a moving object to be tracked over a wide range, at a high processing speed, and with a high accuracy of recognition without increasing the number of image capturing apparatuses installed in one place. 
     The search range determination unit may further determine the limited search range when the movement information indicates that the moving object is traveling toward an imaging region of the second image capturing apparatus. 
     According to the above configuration, the search range may be limited when the moving object appears in the imaging region of the second image capturing apparatus. Therefore, it is possible to detect a moving object more definitely in a determined limited search range. 
     The search range determination unit may further determine a predetermined range in the second image that corresponds to a direction of an imaging region of the first image capturing apparatus to be the limited search range. 
     According to the above configuration, a region where it is highly likely that the moving object will appear in an angle of view of the image capturing apparatus is determined to be a limited search range. Therefore, it is possible to determine the search range more appropriately. 
     The object recognition unit may further perform recognition of the moving object for the limited search range at a timing when the moving object is estimated to reach an imaging region of the second image capturing apparatus. 
     According to the above configuration, the search range is limited at a timing when the moving object is estimated to reach the imaging region of the second image capturing apparatus. It is therefore possible to detect a moving object more definitely. 
     The object detection system may further include a reliability calculation unit configured to calculate, for each type of the moving object, a reliability of recognition of the moving object using the type and the reliability of the moving object obtained as a result of object recognition performed by the object recognition unit and the type and the reliability of the moving object included in the movement information. 
     In this way, the amount of data regarding the reliability calculated using the plurality of pieces of object information is larger than that regarding the reliability obtained in object recognition processing performed by the object recognition unit. Therefore, the accuracy of the reliability calculated using the plurality of pieces of object information becomes higher than that of the reliability obtained in object recognition processing performed by the object recognition unit. Therefore, with the above configuration, it becomes possible to improve the accuracy of the reliability. 
     The reliability calculation unit may further calculate, for each type of the moving object, a reliability by calculating an average of the reliability of the moving object obtained as a result of object recognition performed by the object recognition unit and the reliability of the moving object included in the movement information. 
     According to the above configuration, for each determined type of the moving object, a reliability in which a plurality of reliabilities are taken into account is calculated. Therefore, it is possible to calculate a reliability more appropriately. 
     According to the present disclosure, it is possible to provide an object detection system, an object detection method, and a program capable of tracking a moving object over a wide range, at a high processing speed, and with a high accuracy of recognition without increasing the number of image capturing apparatuses installed in one place. 
     The above and other objects, features and advantages of the present disclosure will become more fully understood from the detailed description given hereinbelow and the accompanying drawings which are given by way of illustration only, and thus are not to be considered as limiting the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a diagram showing an object detection system according to a first embodiment; 
         FIG.  2    is a diagram showing a hardware configuration of an object detection apparatus according to the first embodiment; 
         FIG.  3    is a diagram illustrating a state in which object detection apparatuses according to the first embodiment are installed in a road network; 
         FIG.  4    is a diagram illustrating object information according to the first embodiment; 
         FIG.  5    is a diagram illustrating movement information according to the first embodiment; 
         FIG.  6    is a diagram illustrating an image captured by an image capturing apparatus according to the first embodiment; 
         FIG.  7    is a diagram illustrating an image captured by the image capturing apparatus according to the first embodiment; 
         FIG.  8    is a block diagram showing a configuration of an information processing apparatus according to the first embodiment; 
         FIG.  9    is a flowchart showing an object detection method executed by an object detection system according to the first embodiment; 
         FIG.  10    is a flowchart showing an object detection method executed by the object detection system according to the first embodiment; and 
         FIG.  11    is a diagram for describing a specific example of processing of the object detection system according to the first embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     First Embodiment 
     Hereinafter, with reference to the drawings, embodiments of the present disclosure will be described. Note that the same symbols are assigned to substantially the same components. 
       FIG.  1    is a diagram showing an object detection system  1  according to a first embodiment. The object detection system  1  includes a management server  50  and plurality of object detection apparatuses  100  ( 100 A- 100 D). The management server  50  and the plurality of object detection apparatuses  100  are connected to each other via a wired or wireless network  2  so that they can communicate with each other. Further, the plurality of object detection apparatuses  100 A- 100 D may be connected to one another via the network  2  in such a way that they can communicate with one another. 
     The management server  50  is, for example, a computer. The management server  50  may be, for example, a cloud server. The management server  50  manages information generated in the object detection apparatuses  100  (object information, movement information etc. that will be described later). The management server  50  may include a database (DB) that stores this information. 
     The object detection apparatuses  100 A- 100 D are arranged at positions different from one another. The object detection apparatuses  100 A- 100 D are arranged, for example, near intersections of a road. Further, the object detection apparatuses  100 A- 100 D may be arranged near the road at intervals of, for example, 50 m-100 m. The object detection apparatus  100  is, for example, an infrastructure sensor including a camera, a sensor or the like. The object detection apparatus  100  detects an object, in particular, a moving object, which is a movable object.  FIG.  1    shows four object detection apparatuses  100 A- 100 D. However, the number of object detection apparatuses  100  is not limited to four and may be any number equal to or larger than two. 
       FIG.  2    is a diagram showing a hardware configuration of the object detection apparatus  100  according to the first embodiment. The object detection apparatus  100  includes an image capturing apparatus  101  and an information processing apparatus  110 . That is, the object detection apparatuses  100 A- 100 D respectively include image capturing apparatuses  101 A- 101 D. The image capturing apparatus  101  may be physically separated from the information processing apparatus  110 . In this case, the image capturing apparatus  101  is connected to the information processing apparatus  110  using a wire or wirelessly so that they can communicate with each other. 
     The image capturing apparatus  101  is, for example, a camera body. The image capturing apparatus  101  captures images of (photographs) a predetermined region (an imaging region) that corresponds to a position where the image capturing apparatus  101  (the object detection apparatus  100 ) is installed. The imaging region corresponds to an angle of view (a field of view; captured range) of the image capturing apparatus  101 . The image capturing apparatus  101  can be regarded as an infrastructure sensor. The image capturing apparatus  101  may be a visible light camera, a three-dimensional camera (a point cloud camera) such as LiDAR (Light Detection and Ranging), or an infrared camera. 
     The information processing apparatus  110  performs object recognition processing on an image captured by the image capturing apparatus  101 . Then the information processing apparatus  110  generates object information indicating a result of object recognition. The details thereof will be described later. In the following, the term “image” may also indicate “image data indicating an image”, which indicates a target to be processed in information processing. Further, the image may be a still image or a moving image. 
     The information processing apparatus  110  includes, as a main hardware configuration, a Central Processing Unit (CPU)  102 , a Read Only Memory (ROM)  104 , a Random Access Memory (RAM)  106 , and an interface unit  108  (IF; Interface). The CPU  102 , the ROM  104 , the RAM  106 , and the interface unit  108  are connected to one another via a data bus or the like. The management server  50  may include a configuration that is substantially the same as the hardware configuration of the information processing apparatus  110  described above. 
     The CPU  102  has a function as an arithmetic apparatus that performs control processing, arithmetic processing and the like. The ROM  104  includes a function of storing a control program, an arithmetic program and the like executed by the CPU  102 . The RAM  106  includes a function of temporarily storing process data and the like. The RAM  106  may include a database. Accordingly, the database may be implemented in the information processing apparatus  110 . The interface unit  108  receives or outputs signals from or to an external device using a wire or wirelessly. Further, the interface unit  108  accepts an operation for inputting data performed by a user and displays information for the user. 
       FIG.  3    is a diagram illustrating a state in which the object detection apparatuses  100  according to the first embodiment are arranged in a road network  4 . An outline of this embodiment will be described with reference to  FIG.  3   . The road network  4  includes a road  30 . In the example shown in  FIG.  3   , the object detection apparatuses  100  (the image capturing apparatuses  101 ) are arranged near intersections  40 . The image capturing apparatuses  101  of the object detection apparatuses  100  capture images of imaging regions  42  that correspond to the intersections  40 . Then the information processing apparatuses  110  of the object detection apparatuses  100  detect (recognize) a moving object that is present in the imaging regions  42  using images captured by the image capturing apparatus  101 . The information processing apparatuses  110  perform object recognition using, for example, a machine learning algorithm such as deep learning. The moving object, which is, for example, a vehicle, may be a pedestrian. The vehicle includes, for example, a bicycle, a motorcycle, an automobile, a bus, and a heavy truck. 
     For example, the object detection apparatus  100 A (the image capturing apparatus  101 A) is arranged, for example, near an intersection  40 A. Then the image capturing apparatus  101 A of the object detection apparatus  100 A captures images of an imaging region  42 A that corresponds to the intersection  40 A. Then the object detection apparatus  100 A detects (recognizes) a moving object that is present in the imaging region  42 A using the images captured by the image capturing apparatus  101 A. 
     Further, the object detection apparatus  100 B (the image capturing apparatus  101 B) is arranged near an intersection  40 B that is adjacent to the intersection  40 A. Then the image capturing apparatus  101 B of the object detection apparatus  100 B captures images of an imaging region  42 B that corresponds to the intersection  40 B. The object detection apparatus  100 B then detects (recognizes) a moving object that is present in the imaging region  42 B using the images captured by the image capturing apparatus  101 B. 
     Further, the object detection apparatus  100 C (the image capturing apparatus  101 C) is arranged near an intersection  40 C that is adjacent to the intersection  40 B. Then the image capturing apparatus  101 C of the object detection apparatus  100 C captures images of an imaging region  42 C that corresponds to the intersection  40 C. Then the object detection apparatus  100 C detects (recognizes) a moving object that is present in the imaging region  42 C using the images captured by the image capturing apparatus  101 C. 
     Further, the object detection apparatus  100 D (the image capturing apparatus  101 D) is arranged near an intersection  40 D that is adjacent to the intersection  40 C. Then the image capturing apparatus  101 D of the object detection apparatus  100 D captures images of an imaging region  42 D corresponding to the intersection  40 D. The object detection apparatus  100 D then detects (recognizes) a moving object that is present in the imaging region  42 D using the images captured by the image capturing apparatus  101 D. 
     Assume, for example, that a moving object S moves along the road  30  in the order of the intersection  40 A, the intersection  40 B, the intersection  40 C, and the intersection  40 D. In this case, the moving object S first enters the intersection  40 A, passes through the intersection  40 A, and travels toward the intersection  40 B. When the moving object S traveling toward the intersection  40 A enters the imaging region  42 A (an angle of view of the image capturing apparatus  101 A), this moving object S is detected (recognized) by the object detection apparatus  100 A. When the moving object S further moves and passes through the intersection  40 A, the moving object S exits the imaging region  42 A. In this case, the moving object S is no longer recognized by the object detection apparatus  100 A. 
     When the moving object S further moves toward the intersection  40 B from the intersection  40 A, the moving object S enters the imaging region  42 B from the direction of the intersection  40 A. In this case, the moving object S is detected (recognized) by the object detection apparatus  100 B. When the moving object S then exits the imaging region  42 B, the moving object S is no longer recognized by the object detection apparatus  100 B. 
     Likewise, when the moving object S moves toward the intersection  40 C from the intersection  40 B, the moving object S enters the imaging region  42 C from the direction of the intersection  40 B. In this case, the moving object S is detected (recognized) by the object detection apparatus  100 C. When the moving object S exits the imaging region  42 C, the moving object S is no longer recognized by the object detection apparatus  100 C. 
     Likewise, when the moving object S further moves toward the intersection  40 D from the intersection  40 C, the moving object S enters the imaging region  42 D from the direction of the intersection  40 C. In this case, the moving object S is detected (recognized) by the object detection apparatus  100 D. When the moving object S exits the imaging region  42 D, the moving object S is no longer recognized by the object detection apparatus  100 D. 
     Further, the information processing apparatus  110  of the object detection apparatus  100  generates object information, which is information regarding the recognized moving object. The object information may be generated every time the moving object is recognized. Therefore, during a period in which one moving object continues to be recognized by the object detection apparatus  100 , a plurality of pieces of object information may be generated for this moving object. Then object information regarding one moving object is generated in each of the plurality of object detection apparatuses  100 A- 100 D. 
       FIG.  4    is a diagram illustrating the object information according to the first embodiment. The object information includes, for example, an object ID, which is an identifier of the moving object, a type and reliability of the moving object, a recognition time, a recognition position (recognition latitude and recognition longitude), a recognition orientation, a recognition speed, and a feature amount. Further, when the moving object is a vehicle and can be recognized by the information processing apparatus  110 , the object information may include the type of the vehicle, the color of the vehicle body, and the vehicle body number (number plate information). 
     The “recognition time” here indicates the time when object recognition processing has been performed for the corresponding moving object. The “recognition position” indicates the geographical position of the moving object when the object recognition processing has been performed. The “recognition position” may be calculated, for example, from the position of pixels that correspond to the moving object in the image captured by the image capturing apparatus  101 . The recognition position may be calculated by, for example, associating a pixel position in an image captured by the image capturing apparatus  101  with a position on the road in advance. The “recognition orientation” indicates the moving direction (e.g., North, South, East, and West) of the moving object when the object recognition processing has been performed. The recognition orientation may be calculated by, for example, associating a direction in the image captured by the image capturing apparatus  101  with the orientation in advance and detecting in which direction on the image the moving object has moved. The “recognition speed” indicates the moving speed of the moving object when the object recognition processing has been performed. The recognition speed may be calculated, for example, by associating a pixel position in the image captured by the image capturing apparatus  101  with a position on the road in advance and calculating the difference between the positions of the moving object on the road per unit time. 
     Further, the “type” is a category (class) of the moving object determined in the object recognition processing. The type of the moving object is, for example, a heavy truck, a bus, an automobile, a motorcycle, a bicycle, or a pedestrian. Further, the “reliability” indicates the likelihood that the type of the moving object is likely to be correct (class probability; reliability score), which is determined in the object recognition processing. In the object information, the number of sets of the type and the reliability is not limited to one. For example, the type and the reliability may be expressed, for example, as “heavy truck: 0.3, bus: 0.7”. The “feature amount” indicates features of the moving object extracted from the image captured by the image capturing apparatus  101 . The feature amount may be extracted in the object recognition processing and may be used to determine the type of the object. Further, “the type of the vehicle” may be determined using, for example, a recognition dictionary in which the feature amount is associated with the type of the vehicle. Further, the vehicle body number may be extracted using, for example, Optical Character Recognition (OCR). 
     Further, when the moving object moves and exits the corresponding imaging region  42 , the information processing apparatus  110  generates movement information (passage information) including object information generated just before the moving object exits the imaging region  42  and the installation position information regarding the position where the image capturing apparatus  101  is installed. Therefore, it can be said that the movement information is information regarding the moving object that has moved away from the imaging region  42 . The movement information may be generated, for example, at predetermined time intervals. 
       FIG.  5    is a diagram illustrating the movement information according to the first embodiment. The movement information includes installation position information, current situation information, and object information. The installation position information is information regarding the place where the object detection apparatus  100  (the image capturing apparatus  101 ) is installed. In the example shown in  FIG.  5   , the installation position information is information regarding the intersection  40 . When, for example, the object detection apparatus  100 A generates the movement information, the installation position information is information regarding the intersection  40 A. The installation position information includes, for example, the identification number of the intersection  40  and installation position information (installation position latitude and installation position longitude) indicating the position of the intersection  40 . The identification number may be an identification number of the object detection apparatus  100  or the image capturing apparatus  101 . Further, the installation position information may be positional information of the image capturing apparatus  101  or the imaging region  42 . 
     The current situation information indicates the current situation in the intersection  40 . When, for example, the object detection apparatus  100 A generates movement information, the current situation information indicates the situation in the intersection  40 A. The current situation information may indicate the current time, the operating state, the degree of congestion in the intersection  40 , and the number of passing objects, which is the number of objects passing through the intersection  40 . 
     The object information corresponds to the object information illustrated in  FIG.  4   . The object information included in the movement information relates to a moving object that is no longer recognized since it has exited the angle of view (the imaging region  42 ) of the image capturing apparatus  101 . Specifically, the object information included in the movement information corresponds to the object information generated last before the corresponding moving object exits the angle of view (the imaging region  42 ) of the image capturing apparatus  101 . In other words, the object information included in the movement information is object information generated just before the corresponding moving object exits the angle of view (the imaging region  42 ) of the image capturing apparatus  101 . Therefore, the recognition time, the recognition position, the recognition orientation, and the recognition speed may be a final recognition time, a final recognition position, a final recognition orientation, and a final recognition speed, respectively. 
     The object detection apparatus  100  (the information processing apparatus  110 ) may transmit the generated movement information to an object detection apparatus  100  arranged near an intersection  40  adjacent to the intersection  40  corresponding to itself via the network  2 . In this case, the object detection apparatus  100  arranged near the adjacent intersection  40  described above may acquire the movement information from the object detection apparatus  100 . Further, the information processing apparatus  110  may store the generated movement information in a database of the management server  50  via the network  2 . In this case, the object detection apparatus  100  arranged near the adjacent intersection  40  described above may acquire (receive) the movement information from the management server  50 . That is, the object detection apparatus  100  (e.g., the object detection apparatus  100 B) acquires movement information regarding the moving object included in an image (first image) captured by one image capturing apparatus  101  (e.g., the image capturing apparatus  101 A; the first image capturing apparatus) among a plurality of image capturing apparatuses  101 . 
     Then, the object detection apparatus  100  that has acquired the movement information determines a limited search range in an image (second image) captured by an image capturing apparatus  101  (second image capturing apparatus) among a plurality of image capturing apparatuses  101  in accordance with the above movement information. The object detection apparatus  100 B that has acquired the movement information determines, for example, a limited search range in the image (second image) captured by the image capturing apparatus  101 B (the second image capturing apparatus) in accordance with the above movement information. Then, this object detection apparatus  100  (e.g., the object detection apparatus  100 B) performs object recognition of the moving object for the limited search range. Limiting the search range in the object recognition in this manner is referred to as a search limitation. 
     Assume, for example, that the moving object S passes through the intersection  40 A and travels toward the intersection  40 B. In this case, the object detection apparatus  100 A generates object information regarding the moving object S when the moving object S is present in the imaging region  42 A. When the moving object S exits from the imaging region  42 A, the object detection apparatus  100 A generates movement information regarding the moving object S. In this case, the object detection apparatus  100 A may transmit the movement information to the adjacent object detection apparatus  100  (the object detection apparatus  100 B and an object detection apparatus (not shown) which is opposite to the object detection apparatus  100 B). Further, the object detection apparatus  100 A may transmit the movement information to the management server  50 . 
     On the other hand, the object detection apparatus  100 B acquires the movement information generated by the object detection apparatus  100 A from the object detection apparatus  100 A or the management server  50 . At this time, the object detection apparatus  100 B estimates that the moving object S will reach the imaging region  42 B from the direction of the intersection  40 A (the imaging region  42 A) at time tb. That is, the object detection apparatus  100 B estimates the timing when the moving object S reaches the imaging region  42 B from the direction of the intersection  40 A (the imaging region  42 A). At this time, the object detection apparatus  100 B limits the search range of the moving object to a predetermined region in the direction of the intersection  40 A in the image captured by the image capturing apparatus  101 B, at time tb. In this manner, the object detection apparatus  100 B determines a limited search range in the image (second image) captured by the image capturing apparatus  101 B. Then, the object detection apparatus  100 B performs object recognition of the moving object S in the limited search range, at time tb. 
       FIGS.  6  and  7    are diagrams each illustrating an image captured by the image capturing apparatus  101  according to the first embodiment.  FIGS.  6  and  7    each illustrate an image ImB at the intersection  40 B captured by the image capturing apparatus  101 B. In the image ImB, the back side corresponds to the direction of the intersection  40 A and the front side corresponds to the direction of the intersection  40 C. Then the object detection apparatus  100 B sets a limited search range  60  as illustrated in  FIG.  7    when the moving object S reaches the angle of view (the imaging region  42 B) of the image capturing apparatus  101 B from the direction of the intersection  40 A. Then the object detection apparatus  100 B performs object recognition in the limited search range  60  illustrated in  FIG.  7    at a timing when the moving object S appears in the angle of view (the imaging region  42 B) of the image capturing apparatus  101 B from the direction of the intersection  40 A. 
     Now, effects of the object detection system  1  according to the first embodiment will be described along with the problem according to the above-described technique disclosed in Japanese Unexamined Patent Application Publication No. 2019-165501. When, for example, a moving object is recognized using a visible light camera, the object recognition is performed based on a feature amount of a pixel data group for each still image frame forming a video image in principle. Therefore, the higher the image quality becomes, the larger the range to be recognized becomes and the longer the processing time for each still image frame becomes. Further, due to characteristics of a visible light camera, the position and the size of the object on a projected still image frame vary greatly depending on the position of the object to be recognized (the distance or the angle from the camera). It is therefore difficult to improve the accuracy of recognition. When it is determined that the recognition degree (reliability score) is low in the object recognition, it is assumed that there is no object (missed recognitions). 
     In the aforementioned technique disclosed in Japanese Unexamined Patent Application Publication No. 2019-165501, when one target object is tracked by a plurality of cameras installed in one place, the result of the tracking in one of the plurality of cameras whose reliability is higher than those of the other ones is employed, thereby improving the accuracy of the tracking. Therefore, detection and tracking by a plurality of cameras need to be performed at substantially the same time, and the technique disclosed in Japanese Unexamined Patent Application Publication No. 2019-165501 cannot be applied to tracking of a moving object at different capturing times, that is, tracking of the moving object by cameras located away from one another. Therefore, in the technique disclosed in Japanese Unexamined Patent Application Publication No. 2019-165501, it is difficult to track an object over a wide range. 
     Further, when object recognition is performed using a video image, the number of images whose object needs to be recognized per unit time becomes enormous. Furthermore, as described above, when the search range (the size of this range) increases and the number of types of objects to be detected increases as the quality of the image becomes higher, the object recognition processing may become processing with an extremely heavy load. 
     On the other hand, when the moving object is tracked over a wide range, the object recognition processing is preferably performed by edge computers (or computers on a network aggregated to some extent) installed at places where cameras are installed in view of the problem of a network cost (load). By executing the object recognition processing in a large number of places, it is possible to grasp the movement state of the object or grasp the traffic volume over a wide range. While it is required to arrange a large number of edge computers in order to execute the object recognition processing in a large number of places, it is difficult to improve hardware specifications of each edge computer in view of the cost and the like. It is therefore required to reduce the amount of information to be processed and perform object recognition during an appropriate process time while preventing the accuracy of the object recognition from being reduced in the object recognition processing without improving the hardware specifications. 
     On the other hand, the object detection system  1  according to the first embodiment acquires movement information, which is information regarding the moving object included in, for example, the first image captured by the image capturing apparatus  101 A (first image capturing apparatus) among the plurality of image capturing apparatuses  101  arranged in positions different from one another. Further, the object detection system  1  determines the limited search range in, for example, the second image captured by the image capturing apparatus  101 B among the plurality of image capturing apparatuses  101  in accordance with the acquired movement information. Then the object detection system  1  performs recognition of the moving object for the limited search range in the second image. According to this configuration, the object detection system  1  according to the first embodiment detects a moving object only for a range where the moving object is highly likely to appear. Therefore, the detection speed becomes higher and the accuracy of the detection becomes higher than those in the case in which the whole image is searched. Further, since the plurality of image capturing apparatuses  101  are arranged at positions different from one another, a moving object can be detected over a wide range. Therefore, the object detection system  1  according to the first embodiment is able to track a moving object over a wide range, at a high processing speed, and with a high accuracy of recognition without increasing the number of image capturing apparatuses installed in one place. 
     Next, a configuration of the information processing apparatus  110  will be described. 
       FIG.  8    is a block diagram showing a configuration of the information processing apparatus  110  according to the first embodiment. The information processing apparatus  110  includes a movement information acquisition unit  112 , a search limitation determination unit  114 , and a search range determination unit  116 . Further, the information processing apparatus  110  includes an image acquisition unit  122 , an object recognition unit  124 , an object information extraction unit  126 , a reliability calculation unit  128 , a movement information generation unit  130 , and a movement information transmission unit  132 . These components may be implemented, for example, by the CPU  102  executing a program stored in the ROM  104 . Further, a necessary program may be stored in an arbitrary non-volatile storage medium and installed as necessary, whereby each component may be implemented. Each component is not limited to be implemented by software as described above and may be implemented by hardware such as some sort of circuit element. Further, one or more of the above components may be implemented by physically separate hardware. For example, one or more of the above components may be provided by the management server  50 . 
     In the following description of the components, the object detection apparatus  100  (the image capturing apparatus  101 ) that corresponds to the information processing apparatus  110  including each component (e.g., the movement information acquisition unit  112 ) may be simply referred to as an “own object detection apparatus  100  (own image capturing apparatus  101 )”. Further, the intersection  40  (the imaging region  42 ) that corresponds to the own object detection apparatus  100  may be simply referred to as “the corresponding intersection  40  (the corresponding imaging region  42 )” or “own intersection  40  (own imaging region  42 )”. Further, an object detection apparatus  100  (the image capturing apparatus  101 ) that corresponds to an intersection  40  that is adjacent to the intersection  40  that corresponds to the own object detection apparatus  100  may be simply referred to as an “adjacent object detection apparatus  100  (adjacent image capturing apparatus  101 )” or “object detection apparatus  100  (image capturing apparatus  101 ) adjacent to the own object detection apparatus  100 ”. Further, the imaging region  42  (the intersection  40 ) that corresponds to the adjacent object detection apparatus  100  may be simply referred to as an “adjacent imaging region  42  (adjacent intersection  40 )”. 
     The movement information acquisition unit  112  is configured to acquire the aforementioned movement information. The movement information acquisition unit  112  may receive, for example, the movement information from an object detection apparatus  100  that corresponds to an intersection  40  that is adjacent to the intersection  40  that corresponds to the own object detection apparatus  100  via the network  2 . In this case, the movement information acquisition unit  112  may store the acquired movement information in the database of the management server  50 . Further, when the movement information is stored in the database of the management server  50 , the movement information acquisition unit  112  may receive the movement information from the management server  50  via the network  2 . 
     The movement information acquisition unit  112  may acquire, for example, at a timing when the movement information that corresponds to an intersection  40  that is adjacent to the intersection  40  that corresponds to the own object detection apparatus  100  has been generated, this movement information. When, for example, movement information generated in one object detection apparatus  100  has been transmitted from this object detection apparatus  100 , the management server  50  may transmit the movement information to an object detection apparatus  100  that correspond to an intersection  40  that is adjacent to the intersection  40  that corresponds to the above movement information. Accordingly, the movement information acquisition unit  112  is able to acquire, at the timing when the movement information that corresponds to an intersection  40  that is adjacent to the intersection  40  that corresponds to the own object detection apparatus  100  has been generated, this movement information. Alternatively, the movement information acquisition unit  112  may acquire the movement information at predetermined intervals. 
     The search limitation determination unit  114  is configured to determine whether or not the acquired movement information indicates that the moving object is traveling toward the own imaging region  42 . Then the search limitation determination unit  114  is configured to determine that a search limitation will be imposed when it is determined that the acquired movement information indicates that the moving object is traveling toward the own imaging region  42 . 
     Specifically, the search limitation determination unit  114  determines whether or not object recognition will be performed in a limited search range in the own object detection apparatus  100 . That is, the search limitation determination unit  114  determines whether or not a search limitation will be imposed when object recognition is performed in the future in the own object detection apparatus  100 . Specifically, the search limitation determination unit  114  determines whether or not the moving object is traveling toward the intersection  40  (the imaging region  42 ) that corresponds to the own object detection apparatus  100  using the movement information generated in the adjacent object detection apparatus  100 . For example, the search limitation determination unit  114  determines whether or not one moving object is traveling toward the intersection  40  that corresponds to the own object detection apparatus  100  from the final recognition orientation and the final recognition position (the final recognition latitude and the final recognition longitude) of the object information included in the movement information. 
     When it has been determined that one moving object is traveling toward the imaging region  42  that corresponds to the own object detection apparatus  100 , the search limitation determination unit  114  estimates the timing when this moving object reaches the imaging region  42  that corresponds to the own object detection apparatus  100  (arrival time). Specifically, the search limitation determination unit  114  calculates the distance between the final recognition position of the object information included in the movement information and the position of the own imaging region  42 . Then the search limitation determination unit  114  calculates the timing when the moving object reaches the imaging region  42  that corresponds to the own object detection apparatus  100  (arrival time) from the final recognition time and the final recognition speed of the object information included in the movement information. Specifically, the search limitation determination unit  114  calculates the time required to travel the calculated distance at the final recognition speed. Then the search limitation determination unit  114  adds the calculated time to the final recognition time, thereby calculating the timing when the moving object reaches the imaging region  42  that corresponds to the own object detection apparatus  100  (arrival time). 
     In this case, the search limitation determination unit  114  may determine that the moving object will appear in the own imaging region  42  at the estimated timing from the direction of the intersection  40  that corresponds to the object detection apparatus  100  that has generated the movement information. Then the search limitation determination unit  114  determines that a search limitation will be imposed at the estimated timing. Further, the search limitation may be performed for a predetermined period of time including the estimated timing. The search limitation determination unit  114  may store the result of the determination in the database of the management server  50 . Alternatively, the search limitation determination unit  114  may store the result of the determination in the database implemented in the information processing apparatus  110 . 
     The search range determination unit  116  is configured to determine the limited search range in the image captured by the image capturing apparatus  101  of the own object detection apparatus  100  in accordance with the acquired movement information. Specifically, the search range determination unit  116  determines the search range when the acquired movement information indicates that the moving object is traveling toward the own imaging region  42 . More specifically, the search range determination unit  116  determines the limited search range in an image captured by the own object detection apparatus  100  (the image capturing apparatus  101 ) when the search limitation is imposed at the timing estimated by the search limitation determination unit  114 . Then the search range determination unit  116  determines a predetermined range of the direction of the imaging region  42  of the adjacent object detection apparatus  100  that has generated the movement information in the image captured by the own image capturing apparatus  101  as a search range. 
     For example, like the limited search range  60  shown in  FIG.  7   , the search range determination unit  116  determines a predetermined region including at least a part of the road that leads to the adjacent imaging region  42  (the intersection  40 ) in the image captured by the own image capturing apparatus  101  to be a search range. The search range determination unit  116  may store the result of the determination (determined search range) in the database of the management server  50 . Alternatively, the search range determination unit  116  may store the result of the determination (determined search range) in a database implemented by the information processing apparatus  110 . 
     The image acquisition unit  122  is configured to acquire images from the image capturing apparatus  101 . Specifically, the image acquisition unit  122  acquires images captured by the image capturing apparatus  101  of the own object detection apparatus  100 . The acquisition of images may be executed for each timing when the image capturing apparatus  101  captures an image. 
     The object recognition unit  124  is configured to perform object recognition processing on the acquired image. Specifically, the object recognition unit  124  detects (recognizes) a moving object from the acquired image by, for example, a machine learning algorithm such as deep learning. The object recognition unit  124  extracts an object from the acquired image and calculates, for each extracted object, the type, the position, the speed, the direction in which it moves and the like. The object recognition unit  124  then generates object information ( FIG.  4   ) using the result of the recognition. Then the object recognition unit  124  may transmit the generated object information to the management server  50 , thereby storing this information in the database of the management server  50 . Alternatively, the object recognition unit  124  may store the object information in a database implemented in the information processing apparatus  110 . 
     At this stage, the object ID ( FIG.  4   ) in the object information may be set arbitrarily. That is, even when a moving object recognized before as a result of object recognition performed by the adjacent object detection apparatus  100  is the same as the moving object recognized by the own object detection apparatus  100 , an arbitrary object ID may be given. 
     When there is a search limitation, the object recognition unit  124  is configured to perform object recognition for the limited search range. Specifically, the object recognition unit  124  performs object recognition for the search range determined by the search range determination unit  116  at a timing of the search limitation estimated by the search limitation determination unit  114 . When a search limitation is imposed, the movement information is generated after processing in the object information extraction unit  126  and the reliability calculation unit  128  that will be described later is performed. On the other hand, when there is no search limitation, the object recognition unit  124  is configured to perform object recognition for the whole image. When there is no search limitation, movement information is generated without performing processing in the object information extraction unit  126  and the reliability calculation unit  128  that will be described later. The object recognition unit  124  may determine whether or not there is a search limitation by accessing the database of the management server  50  or the database implemented in the information processing apparatus  110 . 
     As described above, the object information extraction unit  126  performs processing when a search limitation is imposed. The object information extraction unit  126  is configured to extract object information regarding a moving object the same as the moving object recognized by the object recognition processing performed by the object recognition unit  124  from the database. Specifically, the object information extraction unit  126  extracts information on an object including a feature amount whose similarity to the feature amount of the recognized moving object is equal to or larger than a predetermined value as object information regarding a moving object the same as the moving object recognized by object recognition. That is, in this case, the object information extraction unit  126  determines a moving object whose feature amount is similar to that of the recognized moving object as a moving object the same as the recognized moving object. Alternatively, when the type of the vehicle, the color of the vehicle body, and the vehicle body number are included in the object information, the object information extraction unit  126  may extract object information where these information (the type of the vehicle, etc.) coincide with that included in object information regarding the recognized moving object. Note that the object information extraction unit  126  may rewrite the object ID of the object information generated for the recognized moving object into an object ID of the extracted object information. Accordingly, one object ID may be associated with the object information of one moving object. 
     When a search limitation is imposed, it is highly likely that the moving object recognized in the limited search range has been also recognized by an adjacent object detection apparatus  100 . Therefore, it is highly likely that object information regarding a moving object the same as the above moving object has already been stored in a database. On the other hand, when a search limitation is not imposed, it is possible that a moving object that is the same as the recognized moving object may not have been recognized by the adjacent object detection apparatus  100 . Therefore, the object information regarding a moving object the same as the above moving object may not be stored in the database. Therefore, when there is search limitation, the object information extraction unit  126  extracts object information regarding a moving object the same as the recognized moving object from the database. The object information extraction unit  126  may perform the aforementioned processing even when a search limitation is not imposed. However, as described above, since it is possible that object information regarding a moving object the same as the recognized moving object may not be stored in a database, an enormous amount of time may be required to perform extraction processing. That is, it may take time before it is determined that object information regarding a moving object the same as the recognized moving object is not stored in a database. 
     As described above, the reliability calculation unit  128  performs processing when a search limitation is imposed. The reliability calculation unit  128  is configured to re-calculate the reliability of the type of the recognized moving object using the object information extracted by the object information extraction unit  126 . Specifically, the reliability calculation unit  128  re-calculates, for each recognized moving object, the reliability of the type of the moving object using the reliability in object information of a moving object determined to be the same as the above moving object and the reliability in object information regarding the recognized moving object. In other words, the reliability calculation unit  128  updates the reliability regarding the recognized moving object using the reliability regarding a moving object determined to be the same. In other words, the reliability calculation unit  128  calculates the reliability of the recognition of the moving object for each type using the type and the reliability of the moving object obtained as a result of object recognition performed by the object recognition unit  124  and the type and the reliability of the moving object included in the acquired movement information. 
     More specifically, the reliability calculation unit  128  re-calculates the reliability of the moving object S using the reliability of the type obtained by the own object detection apparatus  100  and the reliability of the type obtained by the adjacent object detection apparatus  100 . This re-calculated reliability is called an “integrated reliability”. It is assumed, for example, regarding the moving object S, that the object information generated by the adjacent object detection apparatus  100  indicates that the reliability of a type A 1  (e.g., the “heavy truck”) is Ba 1  and the reliability of a type A 2  (e.g., the “bus”) is Ba 2 . It is further assumed that the object information generated by the own object detection apparatus  100  indicates that the reliability of the type A 1  is Bb 1  and the reliability of the type A 2  is Bb 2 . In this case, the reliability calculation unit  128  calculates, regarding the type A 1 , the integrated reliability by calculating the average of the reliability Ba 1  and the reliability Bb 1 . Likewise, the reliability calculation unit  128  calculates, regarding the type A 2 , the integrated reliability by calculating the average of the reliability Ba 2  and the reliability Bb 2 . For example, the reliability calculation unit  128  may calculate the updated reliability (integrated reliability) by calculating, for each type, the (weighted) harmonic mean of reliabilities. In the above example, the reliability calculation unit  128  calculates, regarding the type A 1 , the integrated reliability by calculating the weighted harmonic mean of the reliability Ba 1  and the reliability Bb 1 . Likewise, the reliability calculation unit  128  calculates, regarding the type A 2 , the integrated reliability by calculating the weighted harmonic mean of the reliability Ba 2  and the reliability Bb 2 . 
     The movement information generation unit  130  is configured to generate movement information ( FIG.  5   ) including object information regarding a moving object that is no longer recognized. Specifically, the movement information generation unit  130  generates movement information including object information regarding a moving object that is no longer recognized by the object recognition unit  124  since it has exited the own intersection  40  (the imaging region  42 ) among all the moving objects that have already been recognized. Note that the movement information generation unit  130  may generate movement information at predetermined time intervals. 
     For example, the movement information generation unit  130  extracts, for each object ID, the latest object information among those whose “recognition time” is earlier than the current time by a predetermined period of time or more from the database. Accordingly, the movement information generation unit  130  is able to extract the latest object information regarding a moving object that has exited the own imaging region  42 . Then the movement information generation unit  130  sets this extracted object information as “object information” in the movement information shown in  FIG.  5   . The movement information generation unit  130  further generates installation position information regarding the own object detection apparatus  100  and the current situation information. Accordingly, the movement information generation unit  130  generates the movement information. Note that the movement information generation unit  130  may store the movement information in the database implemented in the information processing apparatus  110 . 
     The movement information transmission unit  132  is configured to transmit the movement information generated by the movement information generation unit  130 . The movement information acquisition unit  112  may transmit the movement information to the management server  50  via the network  2 , thereby storing the movement information in the database of the management server  50 . Further, the movement information transmission unit  132  may transmit the movement information to an object detection apparatus  100  adjacent to the own object detection apparatus  100  via the network  2 . In this case, the movement information transmission unit  132  may transmit the movement information to an object detection apparatus  100  that corresponds to an intersection  40  which is in the direction in which the moving object travels (final recognition orientation). 
       FIGS.  9  and  10    are flowcharts each showing the object detection method executed by the object detection system  1  according to the first embodiment. The processing shown in  FIG.  9    may be executed, for example, at predetermined time intervals. As described above, the movement information acquisition unit  112  acquires the movement information (Step S 102 ). Specifically, the movement information acquisition unit  112  acquires movement information regarding a moving object included in the image (first image) captured by the adjacent image capturing apparatus  101  (the first image capturing apparatus). 
     As described above, the search limitation determination unit  114  determines whether or not to impose a search limitation when object recognition is performed in the future by the own object detection apparatus  100  using the movement information (Step S 104 ). That is, the search limitation determination unit  114  determines whether or not the moving object regarding the movement information is traveling toward the own imaging region  42 . At this time, as described above, the search limitation determination unit  114  determines the timing when a search limitation is imposed. Then the search limitation determination unit  114  stores the result of the determination in the database. 
     When it is determined that the moving object regarding the movement information is not traveling toward the own imaging region  42 , the search limitation determination unit  114  determines that a search limitation will not be imposed (NO in S 104 ). In this case, the subsequent processing S 106  is omitted. On the other hand, when it is determined that the moving object regarding the movement information is traveling toward the own imaging region  42 , the search limitation determination unit  114  determines that a search limitation will be imposed (YES in S 104 ). In this case, as described above, the search range determination unit  116  determines the limited search range in the image captured by the image capturing apparatus  101  of the own object detection apparatus  100  in accordance with the acquired movement information (Step S 106 ). Then the search range determination unit  116  stores the determined search range in the database. 
     In other words, the search range determination unit  116  determines the search range when the movement information indicates that the moving object is traveling toward the own imaging region  42  (imaging region of the second image capturing apparatus). Accordingly, the search range may be limited when the moving object appears in the own imaging region  42 . Therefore, it is possible to detect a moving object more definitely in a determined search range. 
     Further, as described above, the search range determination unit  116  determines a predetermined range in the direction of the imaging region  42  of the adjacent image capturing apparatus  101  (the first image capturing apparatus) in the image captured by the own image capturing apparatus  101  (the second image) to be a search range. Accordingly, a range where a moving object is highly likely to appear in the angle of view of the image capturing apparatus  101  is determined to be a search range. Therefore, it is possible to determine the search range more appropriately. 
     The processing shown in  FIG.  10    may be executed, for example, every time the own image capturing apparatus  101  captures an image. As described above, the image acquisition unit  122  acquires an image from the own image capturing apparatus  101  (Step S 112 ). The object recognition unit  124  determines whether or not there is a search limitation (Step S 114 ). Specifically, the object recognition unit  124  accesses the database to determine, regarding the own object detection apparatus  100  (the imaging region  42 ), whether or not a result of the determination indicating that a search limitation will be imposed at the current timing is stored. 
     When it has been determined that there is no search limitation at the current timing (NO in S 114 ), the object recognition unit  124  performs object recognition without imposing a search limitation. Therefore, the object recognition unit  124  performs object recognition for the whole image acquired in S 112  (Step S 116 ). Then the object recognition unit  124  generates object information, as described above. Then the object recognition unit  124  stores the generated object information in a database. 
     On the other hand, when it is determined that there is a search limitation at the current timing (YES in S 114 ), the object recognition unit  124  performs object recognition with imposing a search limitation. That is, the object recognition unit  124  extracts a search range from the database and performs object recognition for the extracted search range (Step S 120 ). Then the object recognition unit  124  generates the object information, as described above. Then the object recognition unit  124  stores the generated object information in the database. 
     As described above, at a timing when the moving object is estimated to reach the own imaging region  42  (the imaging region of the second image capturing apparatus), the object recognition unit  124  performs object recognition of the moving object on the search range. Accordingly, at the timing when the moving object is estimated to reach the own imaging region  42 , the search range is limited. It is therefore possible to detect a moving object more definitely. 
     As described above, the object information extraction unit  126  extracts object information regarding a moving object that is the same as the moving object recognized by the object recognition processing performed by the object recognition unit  124  from the database (Step S 122 ). Then the reliability calculation unit  128  calculates the integrated reliability regarding the recognized moving object using the object information extracted by the object information extraction unit  126 , as described above (Step S 124 ). Specifically, the reliability calculation unit  128  calculates, for each type, the reliability (integrated reliability) of the moving object using the type and the reliability of the moving object obtained as a result of object recognition performed by the object recognition unit  124  and the type and the reliability of the moving object included in the movement information. 
     As described above, the reliability calculation unit  128  re-calculates (updates) a reliability, whereby the accuracy of the reliability can be improved. That is, the amount of data regarding the reliability calculated using a plurality of pieces of object information is larger than that in the reliability obtained in the object recognition processing performed by the object recognition unit  124 . Therefore, the accuracy of the reliability calculated using the plurality of pieces of object information may become higher than the accuracy of the reliability obtained in the object recognition processing performed by the object recognition unit  124 . 
     Further, the reliability calculation unit  128  is configured to calculate the reliability (integrated reliability) by calculating, for each type, the average of the reliability of the moving object obtained as a result of object recognition performed by the object recognition unit  124  and the reliability of the moving object included in the movement information. Accordingly, a reliability in which a plurality of reliabilities are taken into account is calculated for each determined type of the moving object. Therefore, the reliability calculation unit  128  is able to calculate the integrated reliability more appropriately. 
     As described above, the movement information generation unit  130  generates movement information (Step S 126 ). When the integrated reliability has been calculated in S 124 , the movement information generation unit  130  generates the movement information in such a way that the reliability of the corresponding moving object in the object information becomes the integrated reliability. The movement information transmission unit  132  transmits the movement information generated in the processing of S 126  (Step S 128 ). In this case, as described above, the movement information acquisition unit  112  may store the movement information in the database of the management server  50 . Further, the movement information transmission unit  132  may transmit the movement information to the adjacent object detection apparatus  100 . 
       FIG.  11    is a diagram for describing a specific example of processing in the object detection system  1  according to the first embodiment.  FIG.  11    illustrates processing in a case in which the moving object S moves from the imaging region  42 A to the imaging region  42 B, as illustrated in  FIG.  3   . It is assumed that the moving object S has been recognized for the first time in the object detection apparatus  100 A. 
     Since the moving object S is recognized for the first time in the object detection apparatus  100 A, at this stage, no object detection apparatus  100  generates movement information including object information regarding the moving object S. Therefore, the object recognition unit  124  of the object detection apparatus  100 A performs object recognition processing on the whole image captured by the image capturing apparatus  101 A without performing a search limitation, thereby recognizing the moving object S (S 116 ). 
     Then, the movement information generation unit  130  of the object detection apparatus  100 A generates movement information Ia including object information Isa of the moving object S (S 126 ). It is assumed that the object ID of the moving object S is Sa in the object information Isa. It is further assumed that the final recognition time of the moving object S is t 1  in the object information Isa. It is further assumed that the final recognition orientation of the moving object S is “a direction of the imaging region B”. Further, the final recognition speed of the moving object S is denoted by v 1 . Further, the feature amount of the moving object S is denoted by Va. 
     Further, it is assumed that the type and the reliability of the moving object S are “heavy truck: 0.6” and “bus: 0.4”. Therefore, in the result of the recognition by the object detection apparatus  100 A, the reliability of the type (category) of the moving object S is higher in “heavy truck” than that in “bus”. That is, in the result of the recognition by the object detection apparatus  100 A, the moving object S is highly likely to be a “heavy truck”. 
     On the other hand, the object detection apparatus  100 B that corresponds to the imaging region  42 B toward which the moving object S travels acquires the movement information Isa. Then the search limitation determination unit  114  of the object detection apparatus  100 B determines that the moving object S is traveling toward the imaging region  42 B from the final recognition orientation of the moving object S “the direction of the imaging region B”. Then the search limitation determination unit  114  of the object detection apparatus  100 B estimates, from the final recognition time t 1  and the final recognition speed v 1  of the moving object S, that the moving object S will reach the imaging region  42 B at time t 2 . That is, the search limitation determination unit  114  of the object detection apparatus  100 B determines that the moving object appears in the angle of view of the image capturing apparatus  101 B at time t 2 . Therefore, the search limitation determination unit  114  of the object detection apparatus  100 B determines that a search limitation will be imposed at time t 2  (S 104 ). Then, the search range determination unit  116  of the object detection apparatus  100 B sets a predetermined range in the direction of the imaging region  42 A as a limited search range  60 , as illustrated in  FIG.  7    (S 106 ). 
     When the image captured by the image capturing apparatus  101 B is acquired at time t 2 , the object recognition unit  124  of the object detection apparatus  100 B performs object recognition for the limited search range (S 120 ). Then the object recognition unit  124  of the object detection apparatus  100 B detects the moving object S and generates the object information Isb regarding the moving object S. 
     It is assumed, in the object information Isb, that the object ID of the moving object S is Sb. It is further assumed that the type and the reliability of the moving object S are “heavy truck: 0.3” and “bus: 0.7”. Therefore, in the result of the recognition by the object detection apparatus  100 B, the reliability of the type (category) of the moving object S is higher in “bus” than that in “heavy truck”. That is, in the result of the recognition by the object detection apparatus  100 B, the moving object S is highly likely to be a “bus”. In this way, the result of the recognition by the object detection apparatus  100 B may be different from the result of the recognition by the object detection apparatus  100 A. This is because the accuracy of the recognition may vary depending on an environmental factor such as the position where the image capturing apparatus  101  is installed with respect to the intersection  40 , visibility at the intersection  40 , brightness in the intersection  40  depending on weather or the like, and the performance of the image capturing apparatus  101 . 
     Further, the object information extraction unit  126  of the object detection apparatus  100 B determines that the feature amount Va in the object information Isa is similar to the feature amount of the moving object S obtained in the object recognition processing in S 120 . Therefore, the object information extraction unit  126  of the object detection apparatus  100 B extracts the object information Isa that corresponds to the moving object S (object ID: Sb) (S 122 ). 
     Then the reliability calculation unit  128  of the object detection apparatus  100 B calculates the integrated reliability of the moving object S using the extracted object information Isa (S 124 ). In this case, the reliability calculation unit  128  of the object detection apparatus  100 B calculates, regarding the type “heavy truck”, that a harmonic mean of the reliability “0.6” in the object information Isa and the reliability “0.3” in the object information Isb is “0.4”. The reliability calculation unit  128  of the object detection apparatus  100 B further calculates, regarding the type “bus”, that a harmonic mean of the reliability “0.4” in the object information Isa and the reliability “0.7” in the object information Isb is “0.5”. 
     As described above, the accuracy of the recognition may vary depending on an environmental factor regarding the image capturing apparatus  101 . Therefore, a result of the recognition by one object detection apparatus  100  is not always correct. As described above, in the example shown in  FIG.  11   , in the result of the recognition by the object detection apparatus  100 A, the moving object S is highly likely to be a “heavy truck”, whereas in the result of the recognition by the object detection apparatus  100 B, the moving object S is highly likely to be a “bus”. On the other hand, according to this embodiment, the accuracy of the reliability can be increased by calculating the integrated reliability using the reliability in the plurality of pieces of object information. 
     Further, when the moving object S moves in the imaging region  42 C and the imaging region  42 D, object recognition is performed regarding the moving object S in both the object detection apparatus  100 C and the object detection apparatus  100 D. Therefore, in both the object detection apparatus  100 C and the object detection apparatus  100 D, data of the reliability regarding the moving object S is further obtained. Therefore, by calculating the integrated reliability further using the reliability obtained in object recognition by the object detection apparatus  100 C and the object detection apparatus  100 D, the accuracy of recognition regarding the moving object S can be further increased. That is, when the integrated reliability is calculated using a large amount of data regarding reliability, a value of the integrated reliability regarding the correct type of the moving object S may tend to become large. Therefore, according to this embodiment, the larger the number of intersections  40  (the imaging regions  42 ) that the moving object S passes through becomes, the higher the accuracy of recognition of the moving object S becomes. It is therefore possible to prevent the moving object from being falsely recognized. For example, in the object recognition by the object detection apparatus  100 A, the moving object S, which is actually a bus, may be falsely recognized to be a heavy truck. On the other hand, by calculating the integrated reliability, the reliability (the integrated reliability) of the bus may become high, whereby it is possible to prevent the moving object S from being falsely recognized as a heavy truck. 
     MODIFIED EXAMPLES 
     Note that the present disclosure is not limited to the above-described embodiments and may be changed as appropriate without departing from the spirit of the present disclosure. For example, the order of each step in the flowcharts shown in  FIGS.  9  and  10    can be changed as appropriate. Further, one or more steps of the flowcharts shown in  FIGS.  9  and  10    may be omitted. For example, in  FIG.  10   , the processing of S 122  and S 124  may be omitted. 
     Further, while the object detection method shown in  FIGS.  9  and  10    is executed in the information processing apparatus  110  of the object detection apparatus  100  in the above-described embodiment, this configuration is merely an example. For example, one or more of the processes shown in  FIGS.  9  and  10    may be executed by the management server  50 . For example, the processing of  FIG.  9    may be executed by the management server  50 . Further, all the processes in  FIGS.  9  and  10    may be executed by the management server  50 . In this case, the management server  50  may receive images captured by the image capturing apparatus  101  from each of the object detection apparatuses  100  and perform the aforementioned processing for each of the object detection apparatuses  100 . However, if all the processes are executed in the management server  50 , it is possible that communication loads may increase. Therefore, some of the processes in the object detection method may be preferably executed by the object detection apparatus  100 . For example, the processing regarding the object recognition may be preferably executed in the object detection apparatus  100 . 
     Further, while the object recognition unit  124  detects an arbitrary moving object, this configuration is merely an example. The object recognition unit  124  may recognize only a specific moving object such as a vehicle violating traffic regulations. Further, the image capturing apparatus  101  may not be fixed near the intersection  40 . For example, images of the intersection  40  may be captured by an image capturing apparatus  101  mounted on a drone or the like. 
     Further, in the above-described examples, the program can be stored and provided to a computer using any type of non-transitory computer readable media. Non-transitory computer readable media include any type of tangible storage media. Examples of non-transitory computer readable media include magnetic storage media (such as flexible disks, magnetic tapes, hard disk drives, etc.), optical magnetic storage media (e.g., magneto-optical disks), CD-Read Only Memory (CD-ROM), CD-R, CD-R/W, and semiconductor memories (such as mask ROM, Programmable ROM (PROM), Erasable PROM (EPROM), flash ROM, Random Access Memory (RAM), etc.). The program may be provided to a computer using any type of transitory computer readable media. Examples of transitory computer readable media include electric signals, optical signals, and electromagnetic waves. Transitory computer readable media can provide the program to a computer via a wired communication line (e.g., electric wires, and optical fibers) or a wireless communication line. 
     From the disclosure thus described, it will be obvious that the embodiments of the disclosure may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the disclosure, and all such modifications as would be obvious to one skilled in the art are intended for inclusion within the scope of the following claims.