Information processing device, information processing method, and program

Abstract

Provided is an information processing device (10) having: an acquisition unit (11) that acquires information indicating traffic conditions of a road where an imaging device is positioned, said imaging device imaging an image for detecting a moving body; and a control unit (12) that controls the value of a parameter relating to the imaging of the imaging device, in accordance with the information indicating the traffic conditions that was acquired by the acquisition unit.

Description

Information processing device, information processing method, and computer-readable medium

[0001] The present disclosure relates to an information processing device, an information processing method, and a non-transitory computer-readable medium on which a program is stored.

[0002] Patent Document 1 discloses a technology for capturing an image of a road using a camera installed on the roadside and detecting vehicles traveling on the road from the captured image by image processing, etc. Patent Document 2 discloses a technology for capturing an image with an appropriate focal length and aperture driven in order to capture an image with appropriate exposure of a violating vehicle that runs a red light or exceeds the speed limit.

[0003] JP 2012-198680 JP 07-160989

[0004] However, the techniques described in Patent Documents 1 and 2 do not take into consideration, for example, the appropriate individual settings for each imaging device installed on the roadside.

[0005] In view of the above-mentioned problems, an object of the present disclosure is to provide a technology that allows appropriate individual settings to be made for each imaging device installed on the roadside.

[0006] In a first aspect of the present disclosure, there is provided an information processing device having an acquisition unit that acquires information indicating traffic conditions on a road on which an imaging device that captures images for detecting moving objects is installed, and a control unit that controls values ​​of parameters related to imaging by the imaging device in accordance with the information indicating the traffic conditions acquired by the acquisition unit.

[0007] In addition, a second aspect of the present disclosure provides an information processing method that acquires information indicating traffic conditions on a road on which an imaging device that captures images for detecting moving objects is installed, and controls the values ​​of parameters related to imaging by the imaging device according to the traffic conditions.

[0008] In addition, a third aspect of the present disclosure provides a non-transitory computer-readable medium storing a program that causes a computer to execute a process of acquiring information indicating traffic conditions on a road on which an imaging device that captures images for detecting moving objects is installed, and controlling parameter values ​​related to imaging by the imaging device according to the traffic conditions.

[0009] According to one aspect, it is possible to appropriately set individual settings for each imaging device installed on the roadside.

[0010] Fig. 1 is a diagram showing an example of the configuration of an information processing system according to an embodiment; Fig. 2 is a diagram showing an example of the hardware configuration of an information processing device, a vehicle computer, and a terminal computer according to an embodiment; Fig. 3 is a diagram showing an example of the configuration of an information processing device according to an embodiment; Fig. 4 is a sequence diagram showing an example of processing of an information processing system according to an embodiment; Fig. 5 is a diagram showing an example of information recorded in a parameter setting table according to an embodiment;

[0011] The principles of the present disclosure will be described with reference to some exemplary embodiments. It should be understood that these embodiments are set forth for illustrative purposes only, to aid those skilled in the art in understanding and practicing the present disclosure, without implying any limitation on the scope of the present disclosure. The disclosure described herein can be implemented in various ways other than those described below. In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0012] <System Configuration> Fig. 1 is a diagram showing an example of the configuration of an information processing system 1 according to an embodiment. In Fig. 1, the information processing system 1 includes a monitoring server 20, a traffic signal 30, a traffic signal base station 31, an imaging device 32, a signal control device 33, and an information processing device 10. The information processing system 1 also includes vehicles 50A, 50B, and 50C (hereinafter, simply referred to as "vehicles 50" unless a distinction is required). The information processing system 1 also includes terminals 60A, 60B, 60C, ... (hereinafter, simply referred to as "terminals 60" unless a distinction is required). The number of monitoring servers 20, traffic signals 30, traffic signal base stations 31, imaging devices 32, signal control devices 33, information processing devices 10, vehicles 50, and terminals 60 is not limited to the example shown in Fig. 1.

[0013] The monitoring server 20 and the information processing device 10 are connected to each other so as to be able to communicate with each other via a communication line N such as the Internet, a wireless LAN (Local Area Network), or a mobile phone network.

[0014] The traffic signal 30, the traffic signal base station 31, the photographing device 32, the signal control device 33, and the information processing device 10 may be connected so as to be able to communicate with each other via various signal cables or wireless communication.

[0015] 1, the information processing device 10 is attached to a pole (signal pole) on which the traffic signal 30 is attached, but the technology of the present disclosure is not limited to this. For example, the information processing device 10 may be attached to a pole on which the traffic signal 30 is not attached (for example, a pole on which a road sign or the like is attached, a street light, or a utility pole). Furthermore, the information processing device 10 may be, for example, an edge server provided between the traffic signal base station 31 and a cloud-side device (for example, the monitoring server 20). Furthermore, the information processing device 10 may be, for example, a cloud-side server.

[0016] The traffic signal 30 is, for example, a traffic signal installed on a signal pole at a road intersection or the like, and controls the traffic of vehicles 50 and pedestrians by displaying green, yellow, red, arrows, etc. The traffic signal 30 may include a traffic signal for vehicles and a traffic signal for pedestrians.

[0017] The traffic light base station 31 is a base station installed on a traffic light pole. Note that the term "base station" (BS) used in this disclosure refers to a device that can provide or host a cell or coverage area over which a vehicle 50 or a terminal 60 can wirelessly communicate. Examples of the traffic light base station 31 may include a gNB (NR Node B), a Node B (Node B or NB), an Evolved Node B (eNode B or eNB), etc. Also, examples of the traffic light base station 31 may include a remote radio unit (RRU), a radio head (RH), a remote radio head (RRH), a low-power node (e.g., a femto node, a pico node), etc.

[0018] The wireless communication described in the present disclosure may conform to standards such as 5G (fifth generation mobile communication system, NR: New Radio), 4G (fourth generation mobile communication system), and 3G (third generation mobile communication system). 4G may include, for example, LTE (Long Term Evolution) Advanced, WiMAX2, and LTE. The wireless communication described in the present disclosure may also conform to standards such as Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Global System for Mobile (GSM), and Wireless Local Area Network (WLAN). The wireless communication described in the present disclosure may also be performed according to any generation of wireless communication protocol currently known or developed in the future.

[0019] The image capturing device 32 is installed on a traffic light pole and measures various types of road-related information. The image capturing device 32 may be, for example, a sensor that captures images (two-dimensional or three-dimensional data), such as a camera, LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging), or RADAR (Radio Detection and Ranging). The image capturing device 32 captures images using parameter values ​​specified by the information processing device 10 and transmits the captured images to the information processing device 10.

[0020] The information processing device 10 sets parameter values ​​corresponding to the traffic conditions of the road, etc., in the imaging device 32. The information processing device 10 may also generate information (traffic information) relating to traffic around the traffic signal 30 based on information acquired from, for example, the imaging device 32 and the signal control device 33. The information processing device 10 may then transmit (provide, notify) the generated traffic information to external devices such as the vehicle 50, the terminal 60, and the monitoring server 20 via the traffic light base station 31.

[0021] The vehicle 50 is a vehicle that travels on a road on which a traffic signal 30 is installed. The vehicle 50 performs wireless communication via the traffic signal base station 31 using a wireless communication device mounted on the vehicle 50. Examples of the vehicle 50 include, but are not limited to, an automobile, a motorcycle, a moped, and a bicycle.

[0022] The terminal 60 is carried by a user such as a pedestrian, and performs wireless communication via the traffic light base station 31. Examples of the terminal 60 include, but are not limited to, a smartphone, a user equipment (UE), a mobile phone, a cellular phone, a personal digital assistant (PDA), a portable computer, a game device, a music storage and playback device, a wearable device, and the like.

[0023] The monitoring server 20 monitors traffic conditions and the like based on the information received from the information processing device 10. The monitoring server 20 may be, for example, a server operated by a public institution. The monitoring server 20 may analyze accident conditions, such as traffic accident patterns (types), based on the traffic information provided by the information processing device 10. The information generated by the monitoring server 20 may be provided to, for example, the police, insurance companies, etc.

[0024] The signal control device 33 is installed on a signal pole and controls the traffic signal 30. The signal control device 33 controls the display of the traffic signal 30, such as red, green, or yellow, based on, for example, traffic conditions detected based on images captured by the image capture device 32, instructions from a traffic management center, or preset data.

[0025] 2 is a diagram showing an example of the hardware configuration of the information processing device 10, the monitoring server 20, the computer of the vehicle 50, and the computer of the terminal 60 according to the embodiment. The following description will be given using the information processing device 10 as an example. Note that the hardware configurations of the monitoring server 20, the computer of the vehicle 50, and the computer of the terminal 60 may be the same as the hardware configuration of the information processing device 10 in FIG. 2.

[0026] 2, the information processing device 10 (computer 100) includes a processor 101, a memory 102, and a communication interface 103. These components may be connected via a bus or the like. The memory 102 stores at least a portion of a program 104. The communication interface 103 includes an interface required for communication with other network elements.

[0027] When the program 104 is executed by the processor 101, memory 102, and other components in cooperation with each other, the computer 100 performs at least some of the processing of the embodiments of the present disclosure. The memory 102 may be of any type suitable for a local technology network. The memory 102 may be, by way of non-limiting example, a non-transitory computer-readable storage medium. The memory 102 may also be implemented using any suitable data storage technology, such as semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory, and removable memory. While only one memory 102 is shown in the computer 100, several physically distinct memory modules may be present in the computer 100. The processor 101 may be of any type. The processor 101 may include one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), and, by way of non-limiting example, a processor based on a multi-core processor architecture. The computer 100 may have multiple processors, such as application-specific integrated circuit chips time-slaved to a clock that synchronizes the main processor.

[0028] Embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device.

[0029] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, that execute on a target real or virtual processor or device to perform the processes or methods of the present disclosure. Program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or divided among program modules as desired in various embodiments. The machine-executable instructions of the program modules may be executed in local or distributed devices. In a distributed device, the program modules may be located in both local and remote storage media.

[0030] The program code for executing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus. When the program code is executed by the processor or controller, the functions / acts in the flowcharts and / or implementing block diagrams are performed. The program code may be executed entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine, or entirely on a remote machine or server.

[0031] The program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored on a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disc (DVD), Blu-ray disc or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0032] <Configuration> Next, the configuration of the information processing device 10 according to the embodiment will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration of the information processing device 10 according to the embodiment. The information processing device 10 has an acquisition unit 11 and a control unit 12. These units may be realized by cooperation between one or more programs installed in the information processing device 10 and hardware such as a processor 101 and a memory 102 of the information processing device 10.

[0033] The acquisition unit 11 acquires information indicating the traffic conditions of a road on which an image capturing device 32 is installed, the image capturing device 32 capturing images for detecting moving objects such as a vehicle 50 or a pedestrian. The control unit 12 controls the values ​​of parameters related to image capturing by the image capturing device 32 based on the information acquired by the acquisition unit 11.

[0034] <Processing> An example of processing of the information processing system 1 according to the embodiment will be described with reference to Fig. 4 and Fig. 5. Fig. 4 is a sequence diagram showing an example of processing of the information processing system 1 according to the embodiment. Fig. 5 is a diagram showing an example of information recorded in a parameter setting table 501 according to the embodiment.

[0035] The processes from step S101 to step S103 may be executed periodically, for example. Alternatively, the processes from step S101 to step S103 may be executed only when the traffic situation changes, for example. Alternatively, the processes from step S104 to step S106 may be executed constantly, for example.

[0036] In step S101, the acquisition unit 11 of the information processing device 10 acquires information indicating traffic conditions on roads surrounding an area captured by the imaging device 32. Here, the acquisition unit 11 may determine (estimate, infer) the traffic conditions using AI (artificial intelligence) using deep learning or the like based on the image captured by the imaging device 32. The acquisition unit 11 may also determine the traffic conditions based on various sensors installed on the road or the like. The acquisition unit 11 may also receive information indicating the traffic conditions from the monitoring server 20, for example.

[0037] The information indicating the traffic conditions may include the number of vehicles 50 traveling on the road. This makes it possible to appropriately determine the parameters of the imaging device 32 depending on the number of vehicles 50 that can illuminate the road with headlights or the like, for example, during a relatively dark nighttime period. The information indicating the traffic conditions may also include the types of vehicles 50 traveling on the road. This makes it possible to appropriately determine the parameters of the imaging device 32 depending on the illumination range of headlights or the like for each type of vehicle. Furthermore, for example, if a specific type of vehicle 50 is relatively prone to accidents, it is possible to determine the parameters of the imaging device 32 depending on that vehicle 50.

[0038] The information indicating the traffic conditions may also include the traveling speed of each vehicle 50 traveling on the road. This makes it possible to determine the parameters of the image capturing device 32 according to the traveling speed of the vehicle 50, for example.

[0039] The information indicating the traffic conditions may also include the color of each vehicle 50 traveling on the road. This makes it possible to determine parameters for the image capturing device 32 according to the color of the vehicle 50, for example.

[0040] The information indicating the traffic conditions may also include the lighting status of the traffic lights 30 on the road. The lighting status of the traffic lights may be, for example, the green, yellow, red, or arrow display status of the traffic lights 30. This makes it possible to determine, for example, parameters of the image capturing device 32 according to the color of the traffic lights 30 on the road. For example, when the lighting status of the traffic lights is red, the control unit 12 may slow down the shutter speed because moving objects such as vehicles 50 and pedestrians are stopped. Furthermore, when the lighting status of the traffic lights is green, the control unit 12 may speed up the shutter speed because moving objects such as vehicles 50 and pedestrians are moving.

[0041] Next, the control unit 12 of the information processing device 10 identifies the values ​​(settings) of parameters related to image capture by the image capture device 32 according to the traffic conditions of the road (step S102). The image capture parameters may include, for example, at least one of aperture value, shutter speed, ISO sensitivity, sharpness, and contrast. Here, for example, when the number of vehicles 50 traveling on the road is equal to or less than a threshold, the control unit 12 may increase the exposure (the amount of light captured by the camera) by adjusting at least one setting of aperture value (F-number), shutter speed, and ISO (International Organization for Standardization) sensitivity. This is because, when the number of vehicles 50 traveling on the road is equal to or less than a threshold, the subject is likely to be relatively poorly illuminated by the headlights of each vehicle 50. The ISO sensitivity may be, for example, a guideline value for amplifying the signal from the image sensor of a digital camera.

[0042] Furthermore, the control unit 12 may increase the aperture setting when, for example, the number of vehicles 50 traveling on the road is equal to or greater than a threshold value. This increases the range in focus, potentially improving the accuracy of detecting subjects in areas near the edges of the image. Note that the larger the aperture setting, the less light is taken in and the wider the range in focus.

[0043] Furthermore, for example, when a specific type of vehicle 50 (e.g., a dump truck, a vehicle for elderly people, etc.) is present, the control unit 12 may increase (strengthen) the sharpness (edge ​​enhancement) setting value. This may increase the detection accuracy for, for example, vehicles of a type that are relatively prone to accidents or that will cause relatively greater damage if an accident does occur.

[0044] Furthermore, the control unit 12 may increase the shutter speed setting based on, for example, at least one of a representative value (average value, mode value, or median value) and the highest value of the traveling speed of each vehicle 50 traveling on the road, which may reduce blurring and improve the accuracy of subject detection.

[0045] Furthermore, the control unit 12 may increase the contrast setting value when, for example, the vehicles 50 traveling on the road include a vehicle 50 that is a specific color (for example, gray). This may increase the detection accuracy of a subject that is a similar color to the background, for example.

[0046] Furthermore, the control unit 12 may determine the setting value of the shutter speed based on, for example, the color of the traffic light 30 on the road. In this case, the control unit 12 may increase the setting value of the shutter speed when the light is green, for example, because it is considered that the vehicle 50 is moving. This may reduce blurring, for example, and therefore may increase the accuracy of subject detection.

[0047] (Example of determination according to load on traffic light base station 31) Furthermore, the control unit 12 may specify the value of a parameter related to photography by the photography device 32 according to, for example, traffic conditions and the load on the traffic light base station 31 installed in correspondence with the photography device 32. In this case, the load on the traffic light base station 31 may include at least one of the number of user terminals (e.g., vehicles 50 and terminals 60) present in the range of the traffic light base station 31, the amount of data in uplink communication to the traffic light base station 31, and the amount of data in downlink communication from the traffic light base station 31. Note that a user terminal being present in the range of the traffic light base station 31 refers, for example, to the user terminal registering its location with the traffic light base station 31 in order to perform uplink or downlink communication via the traffic light base station 31.

[0048] For example, assume that the information processing device 10 is a multi-access edge computing (MEC) device or the like and performs various processes resulting from wireless communication, so that the higher the load on the traffic light base station 31, the higher the processing load on the information processing device 10. Furthermore, assume that the information processing device 10 performs object detection processing using AI. In this case, for example, when the load on the traffic light base station 31 is less than a threshold, the control unit 12 may perform object detection using the first trained model.

[0049] When the load on the traffic light base station 31 is equal to or greater than a threshold, the control unit 12 may perform object detection using a second trained model that can perform object detection faster (with less computational effort) than the first trained model. When the load on the traffic light base station 31 is equal to or greater than a threshold, the control unit 12 may identify parameter values ​​according to traffic conditions based on a table for the second trained model, etc.

[0050] (Example of Determining Parameters Based on Traffic Conditions of Nearby Roads) The control unit 12 may also specify parameter values ​​based on traffic conditions of nearby roads. In this case, the acquisition unit 11 may acquire information indicating traffic conditions of the road on which the image capture device 32 is installed and information indicating traffic conditions at intersections adjacent to the intersection on that road. The control unit 12 may then specify parameters related to image capture by the image capture device 32 based on the traffic conditions of the road and the traffic conditions at intersections adjacent to the intersection on that road. This allows the control unit 12 to set parameters based on, for example, the number and traveling speed of vehicles 50 passing through the upcoming intersection in the image capture device 32 before the vehicle 50 arrives on that road.

[0051] The control unit 12 may refer to a parameter setting table 501 in FIG. 5 to identify the setting value of each parameter according to the traffic conditions, etc. In the example of FIG. 5, the parameter setting table 501 records the setting value of each parameter in association with a combination of the camera device ID, the traffic conditions, and the load of the traffic light base station 31. The camera device ID is identification information for the camera device 32. The traffic conditions may be classified (classified or grouped) according to the range of values ​​of each item included in the information indicating the traffic conditions. In this case, for example, the traffic conditions may be classified into three classes: high, normal, and low, depending on the number of vehicles 50 traveling on the road. The information in the parameter setting table 501 may be set in advance by an operator (administrator), etc.

[0052] The information in the parameter setting table 501 may also be recorded by the control unit 12. In this case, the control unit 12 may use AI to detect objects based on images captured with each parameter setting value for each imaging device 32 and for each traffic condition, etc. Then, the control unit 12 may record a group of setting values ​​that provides the highest accuracy in object detection in each image in the parameter setting table 501 in association with the traffic condition, etc. This allows appropriate setting values ​​to be determined for each traffic condition, etc., depending on, for example, the angle of view of the imaging device 32, the background, etc.

[0053] The control unit 12 may specify the parameter value to be set in the image capture device 32 based on, for example, the detection accuracy of a moving object in an image captured by the image capture device 32 using a first parameter value and the detection accuracy of a moving object in an image captured by the image capture device 32 using a second parameter value. In this case, the control unit 12 may determine, for example, the detection accuracy of a vehicle 50, etc., in an image captured by the image capture device 32 using a first parameter value when the road is in a specific traffic condition. Then, the control unit 12 may determine, for example, the detection accuracy of a vehicle 50, etc., in an image captured by the image capture device 32 using a second parameter value when the road is in a specific traffic condition. Then, the control unit 12 may specify, for example, the parameter value with a higher detection accuracy between the first parameter value and the second parameter value as the parameter value to be set for the specific traffic condition. Note that the control unit 12 may use, for example, a reliability (likelihood) value calculated as the likelihood of a vehicle 50 when the vehicle 50 is detected using deep learning or the like, as the value indicating the detection accuracy. In addition, the control unit 12 may identify the parameter values ​​to be set based on the detection accuracy of moving objects using images captured with three or more parameter values, not limited to the first parameter value and the second parameter value.

[0054] Next, the control unit 12 of the information processing device 10 causes the image capturing device 32 to set the identified parameter value (step S103). Here, the control unit 12 may transmit a command to the image capturing device 32 to set the parameter value.

[0055] Next, the photographing device 32 photographs an image using the set parameter values ​​(step S104), and then transmits the photographed image to the information processing device 10 (step S105).

[0056] Next, the control unit 12 of the information processing device 10 detects a moving object based on the captured image (step S106). Here, the control unit 12 may detect (estimate, infer) the type of subject, for example, by AI using deep learning, etc. Then, the control unit 12 may determine, for example, traffic conditions, etc., based on the detection result.

[0057] The control unit 12 may control the values ​​of parameters related to photography in accordance with, for example, information indicating road traffic conditions and information indicating at least one of the time of day and the weather. In this case, for example, when it is nighttime, cloudy, or rainy, and there are few vehicles traveling on the road, the road is dark, so the control unit 12 may control the values ​​of parameters to increase the exposure time.

[0058] The control unit 12 may also control the values ​​of parameters related to photography based on, for example, statistics of road traffic conditions over a predetermined period. In this case, the control unit 12 may control the values ​​of the parameters based on, for example, a statistical value of the number of vehicles per unit time. The statistical value may be, for example, a maximum value, a minimum value, or a representative value (average value, mode, or median).

[0059] Each functional unit (e.g., the control unit 12) of the information processing device 10 may be realized by cloud computing configured with one or more computers. Also, the information processing device 10 and the monitoring server 20 may be configured as an integrated server. Such information processing devices 10 are also included in the examples of the "information processing device" of the present disclosure.

[0060] The present invention is not limited to the above-described embodiment, and can be modified as appropriate within the scope of the invention.

[0061] Some or all of the above embodiments can be described as, but are not limited to, the following supplementary notes. (Supplementary Note 1) An information processing device comprising: an acquisition unit that acquires information indicating traffic conditions on a road on which an image capturing device that captures images for detecting moving objects is installed; and a control unit that controls parameter values ​​related to image capturing by the image capturing device in accordance with the information indicating the traffic conditions acquired by the acquisition unit. (Supplementary Note 2) The information processing device according to Supplementary Note 1, wherein the information indicating traffic conditions includes the number of vehicles traveling on the road. (Supplementary Note 3) The information processing device according to Supplementary Note 1 or 2, wherein the information indicating traffic conditions includes the type of vehicle traveling on the road. (Supplementary Note 4) The information processing device according to Supplementary Note 1 or 2, wherein the information indicating traffic conditions includes the traveling speed of vehicles traveling on the road. (Supplementary Note 5) The information processing device according to Supplementary Note 1 or 2, wherein the information indicating traffic conditions includes the color of vehicles traveling on the road. (Supplementary Note 6) The information processing device according to Supplementary Note 1 or 2, wherein the information indicating traffic conditions includes the lighting status of traffic signals of traffic lights on the road. (Supplementary Note 7) The information processing device according to Supplementary Note 1 or 2, wherein the acquisition unit acquires information indicating traffic conditions on the road and information indicating traffic conditions at intersections adjacent to the intersection of the road, and the control unit controls parameters related to image capture by the image capture device according to the traffic conditions on the road and the traffic conditions at intersections adjacent to the intersection of the road. (Supplementary Note 8) The information processing device according to Supplementary Note 1 or 2, wherein the control unit specifies values ​​of the parameters to be set for specific traffic conditions based on a detection accuracy of the moving object in an image captured by the image capture device with a first parameter value when the road is in the specific traffic conditions, and a detection accuracy of the moving object in an image captured by the image capture device with a second parameter value when the road is in the specific traffic conditions. (Supplementary Note 9) The information processing device according to Supplementary Note 1 or 2, wherein the parameters include at least one of an aperture value, a shutter speed, an ISO sensitivity, sharpness, and contrast. (Supplementary Note 10) The information processing device according to Supplementary Note 1 or 2, wherein the control unit controls the value of the parameter in accordance with the traffic conditions and a load on a base station installed in correspondence with the image capturing device.(Supplementary Note 11) The information processing device according to Supplementary Note 10, wherein the load on the base station includes at least one of the number of user terminals within the service area of ​​the base station, the amount of data in uplink communication to the base station, and the amount of data in downlink communication from the base station. (Supplementary Note 12) An information processing method comprising: acquiring information indicating traffic conditions on a road on which an image capturing device for capturing images to detect moving objects is installed; and controlling values ​​of parameters related to image capturing by the image capturing device according to the traffic conditions. (Supplementary Note 13) A non-transitory computer-readable medium having stored thereon a program that causes a computer to execute a process of acquiring information indicating traffic conditions on a road on which an image capturing device for capturing images to detect moving objects is installed; and controlling values ​​of parameters related to image capturing by the image capturing device according to the traffic conditions.

[0062] REFERENCE SIGNS LIST 1 Information processing system 10 Information processing device 11 Acquisition unit 12 Control unit 20 Monitoring server 30 Traffic signal 31 Traffic signal base station 32 Photography device 33 Signal control device 50 Vehicle 60 Terminal

Claims

1. an acquisition unit that acquires information indicating traffic conditions of a road on which an image capturing device that captures an image for detecting a moving object is installed; a control unit that controls a parameter value related to the image capture of the image capture device in accordance with the information indicating the traffic condition acquired by the acquisition unit; An information processing device having the above.

2. The information indicating the traffic conditions includes the number of vehicles traveling on the road. The information processing device according to claim 1 .

3. The information indicating the traffic conditions includes a type of vehicle traveling on the road. The information processing device according to claim 1 .

4. The information indicating the traffic conditions includes a traveling speed of a vehicle traveling on the road. The information processing device according to claim 1 .

5. the information indicating the traffic conditions includes the colors of vehicles traveling on the road; The information processing device according to claim 1 .

6. The information indicating the traffic conditions includes a lighting status of a traffic signal of the road. The information processing device according to claim 1 .

7. the acquisition unit acquires information indicating a traffic condition of the road and information indicating a traffic condition at an intersection adjacent to the intersection of the road, the control unit controls parameters related to photography of the photographing device in accordance with traffic conditions on the road and traffic conditions at intersections adjacent to the intersection of the road. The information processing device according to claim 1 .

8. the control unit specifies the parameter value to be set for the specific traffic condition based on a detection accuracy of the moving object in an image captured by the imaging device with a first parameter value when the road is in the specific traffic condition, and a detection accuracy of the moving object in an image captured by the imaging device with a second parameter value when the road is in the specific traffic condition; The information processing device according to claim 1 .

9. Acquire information indicating traffic conditions on a road on which an image capturing device for capturing images to detect a moving object is installed; controlling the values ​​of the parameters related to the photographing of the photographing device in accordance with the traffic conditions; Information processing methods.

10. Acquire information indicating traffic conditions on a road on which an image capturing device for capturing images to detect a moving object is installed; controlling the values ​​of the parameters related to the photographing of the photographing device in accordance with the traffic conditions; A program that causes a computer to perform a process.