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

Technical Field

[0001] The present disclosure relates to an information processing apparatus, an information processing method, and a non-temporary computer-readable medium storing a program.

Background Art

[0002] Patent Document 1 discloses a technique of capturing an image on a road with a camera installed on the roadside and detecting a vehicle traveling on the road from the captured image by image processing or the like. Further, Patent Document 2 discloses a technique of capturing a violating vehicle traveling while ignoring a red signal or a violating vehicle traveling at a speed exceeding a speed limit with appropriate exposure, in a state where the focal length and the aperture are appropriately driven.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

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

[0005] An object of the present disclosure is to provide a technique capable of appropriately performing individual settings for each imaging device installed on the roadside in view of the above-described problems.

Means for Solving the Problems

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

[0007] Furthermore, a second aspect of the present disclosure provides an information processing method for acquiring information indicating the traffic conditions on a road where a camera for capturing images to detect moving objects is installed, and controlling the values ​​of parameters related to the camera's imaging according to the traffic conditions.

[0008] Furthermore, in a third aspect relating to this disclosure, a non-temporary computer-readable medium is provided which stores a program that causes a computer to execute a process that acquires information indicating the traffic conditions on a road where a camera for capturing images for detecting moving objects is installed, and controls the values ​​of parameters related to the camera's shooting according to the traffic conditions. [Effects of the Invention]

[0009] From one perspective, it allows for appropriate individual settings to be made for each imaging device installed along the roadside. [Brief explanation of the drawing]

[0010] [Figure 1] This figure shows an example configuration of an information processing system according to the embodiment. [Figure 2] This figure shows an example of the hardware configuration of an information processing device, a vehicle computer, and a terminal computer according to the embodiment. [Figure 3] This figure shows an example of the configuration of an information processing device according to the embodiment. [Figure 4] This is a sequence diagram showing an example of the processing of the information processing system according to the embodiment. [Figure 5] This figure shows an example of the information recorded in the parameter setting table according to the embodiment. [Modes for carrying out the invention]

[0011] The principles of this disclosure will be described with reference to several exemplary embodiments. These embodiments are described for illustrative purposes only and should be understood as helping those skilled in the art to understand and implement this disclosure without implying any limitation on the scope of this disclosure. The disclosures described herein may 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 meanings as those generally understood by those skilled in the art to which this disclosure belongs. Embodiments of the present invention will be described below with reference to the drawings.

[0012] <System Configuration> Figure 1 is a diagram showing an example configuration of an information processing system 1 according to an embodiment. In Figure 1, the information processing system 1 includes a monitoring server 20, a traffic signal 30, a traffic signal base station 31, a camera 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 referred to simply as "vehicle 50" unless otherwise specified). The information processing system 1 also includes terminals 60A, 60B, 60C, ... (hereinafter referred to simply as "terminal 60" unless otherwise specified). Note that the number of monitoring servers 20, traffic signals 30, traffic signal base stations 31, cameras 32, signal control devices 33, information processing devices 10, vehicles 50, terminals 60, etc., is not limited to the example in Figure 1.

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

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

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

[0016] The traffic signal device 30 is installed on a signal pole at, for example, an intersection of a road, and is a traffic signal device that regulates traffic of the vehicle 50 and pedestrians by displays such as blue, yellow, red, and arrows. The traffic signal device 30 may include a traffic signal device for vehicles and a traffic signal device for pedestrians.

[0017] The signal base station 31 is a base station installed on the signal pole. The term "base station" (BS: Base Station) used in the present disclosure refers to a device that can provide or host a cell or coverage in which the vehicle 50 or the terminal 60 can perform wireless communication. Examples of the signal base station 31 may include a gNB (NR Node B), a Node B (NodeB or NB), an Evolved Node B (eNodeB or eNB), and the like. Further, examples of the signal base station 31 may include a Remote Radio Unit (RRU), a Radio Head (RH), a Remote Radio Head (RRH), and a low-power node (for example, a femto node, a pico node), and the like.

[0018] The wireless communication described in this disclosure may comply with standards such as 5G (5th Generation Mobile Communication System, NR: New Radio), 4G (4th Generation Mobile Communication System), 3G (3rd Generation Mobile Communication System), etc. Note that 4G may include, for example, LTE (Long Term Evolution) Advanced, WiMAX2, LTE. Also, the wireless communication described in this disclosure may comply with standards such as Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Global System for Mobile (GSM) for vehicle communication, and Wireless Local Area Network (LAN). Further, the wireless communication of this disclosure may be executed in accordance with any generation of wireless communication protocol that is currently known or will be developed in the future.

[0019] The imaging device 32 is an imaging device installed on a signal pole and measures various types of information related to the road. The imaging device 32 may be a sensor that captures images (2D or 3D data) such as a camera, LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging), RADAR (radio detection and Ranging), etc. The imaging device 32 captures an image with the value of the parameter specified by the information processing device 10 and transmits the captured image to the information processing device 10.

[0020] The information processing device 10 sets the value of the parameter according to the traffic situation of the road, etc. in the imaging device 32. Also, the information processing device 10 may generate information related to the traffic (traffic information) around the traffic signal 30 based on the information obtained from, for example, the imaging device 32 and the signal control device 33, etc. Then, the information processing device 10 may 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 signal machine base station 31.

[0021] Vehicle 50 is a vehicle that travels on a road where traffic signals 30 are installed. Vehicle 50 communicates wirelessly via the traffic signal base station 31 using a wireless communication device mounted on the vehicle 50. Examples of vehicle 50 include, but are not limited to, automobiles, motorcycles, mopeds, and bicycles.

[0022] Terminal 60 is a device carried by users such as pedestrians and that communicates wirelessly via the traffic signal base station 31. Examples of terminal 60 include, but are not limited to, smartphones, user equipment (UE), mobile phones, cellular phones, personal digital assistants (PDAs), portable computers, game devices, music storage and playback devices, and wearable devices.

[0023] The monitoring server 20 monitors traffic conditions, etc., based on information received from the information processing device 10. The monitoring server 20 may be a server operated by, for example, a public institution. The monitoring server 20 may analyze accident situations, such as patterns (types) of traffic accidents, based on 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 traffic conditions detected based on images from the camera 32, instructions from a traffic management center, or preset data.

[0025] <Hardware Configuration> Figure 2 shows an example of the hardware configuration of the information processing device 10, monitoring server 20, vehicle 50 computer, and terminal 60 computer according to the embodiment. In the following description, the information processing device 10 will be used as an example. Note that the hardware configuration of the monitoring server 20, vehicle 50 computer, and terminal 60 computer may be the same as the hardware configuration of the information processing device 10 in Figure 2.

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

[0027] When program 104 is executed in cooperation with the processor 101 and memory 102, etc., the computer 100 performs at least some of the processing of embodiments of this disclosure. Memory 102 may be any type suitable for a local technology network. Memory 102 may, in non-limiting examples, be a non-temporary computer-readable storage medium. 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. Although only one memory 102 is shown for computer 100, computer 100 may have several physically different memory modules. Processor 101 may be any type. Processor 101 may include one or more general-purpose computers, dedicated computers, microprocessors, digital signal processors (DSPs), and, in non-limiting examples, processors based on multicore processor architectures. Computer 100 may have multiple processors, such as application-specific integrated circuit chips that are time-dependent to a clock that synchronizes the main processor.

[0028] Embodiments of the present disclosure may be implemented in hardware or in dedicated circuitry, software, logic, or any combination thereof. Some embodiments may be implemented in hardware, while others may be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device.

[0029] This disclosure also provides at least one computer program product tangibly stored on a non-temporary computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions contained in a program module, and is executed on a device on a target real or virtual processor to perform the processes or methods of this disclosure. The program module includes routines, programs, libraries, objects, classes, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The functionality of the program module may be combined or divided among the program module as desired in various embodiments. The machine-executable instructions of the program module can be executed on a local or distributed device. On a distributed device, the program module can reside on both local and remote storage media.

[0030] Program code for performing the methods of this disclosure may be written in any combination of one or more programming languages. These program codes are provided to a processor or controller of a general-purpose computer, a dedicated computer, or other programmable data processing device. When the program code is executed by the processor or controller, the functions / operations in the flowchart and / or block diagrams it implements are performed. The program code may run entirely on a machine, partially on a machine, partially as a standalone software package, partially on a machine, partially on a remote machine, or entirely on a remote machine or server.

[0031] The program, when loaded into a computer, includes a set of instructions (or software code) for causing the computer to perform one or more of the functions described in the embodiments. The program may be stored on a non-temporary computer-readable medium or a physical storage medium. Examples, but not limited to, 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 disc storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices. The program may be transmitted over a temporary computer-readable medium or a communication medium. Examples, but not limited to, include temporary computer-readable medium or a communication medium that includes electrically, optically, acoustically or otherwise propagating signals.

[0032] <Structure> Next, with reference to Figure 3, the configuration of the information processing device 10 according to the embodiment will be described. Figure 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. Each of these units may be realized through the cooperation of one or more programs installed in the information processing device 10 and hardware such as the processor 101 and memory 102 of the information processing device 10.

[0033] The acquisition unit 11 acquires information indicating the traffic conditions of the road where the imaging device 32, which takes images to detect moving objects such as vehicles 50 and pedestrians, is installed. The control unit 12 controls the values ​​of the imaging parameters of the imaging device 32 based on the information acquired by the acquisition unit 11.

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

[0035] The processes from step S101 to step S103 may be executed at regular intervals, for example. Alternatively, the processes from step S101 to step S103 may be executed only when traffic conditions change, for example. Furthermore, the processes from step S104 to step S106 may be executed continuously, for example.

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

[0037] The information indicating traffic conditions may include the number of vehicles 50 traveling on the road. This allows for the appropriate determination of the parameters of the camera 32 according to the number of vehicles 50 that can illuminate the road with their headlights, etc., especially during relatively dark nighttime hours. The information indicating traffic conditions may also include the types of vehicles 50 traveling on the road. This allows for the appropriate determination of the parameters of the camera 32 according to the illumination range of the headlights, etc., for each type of vehicle. Furthermore, for example, if a particular type of vehicle 50 is relatively prone to accidents, the parameters of the camera 32 can be determined according to that vehicle 50.

[0038] Furthermore, the information indicating traffic conditions may include the speed of each vehicle 50 traveling on the road. This allows, for example, the parameters of the camera 32 to be determined according to the speed of the vehicles 50.

[0039] Furthermore, the information indicating traffic conditions may include the color of each vehicle 50 traveling on the road. This allows, for example, the parameters of the camera 32 to be determined according to the color of the vehicle 50.

[0040] Furthermore, the information indicating traffic conditions may include the status of the traffic signals 30 on the road. The status of the signals may be, for example, the display status of green, yellow, red, and arrows on the traffic signals 30. This allows, for example, the parameters of the camera 32 to be determined according to the color of the traffic signals 30 on the road. For example, if the status of the signal is red, the control unit 12 may slow down the shutter speed because moving objects such as vehicles 50 and pedestrians are stationary. Conversely, if the status of the signal is green, the control unit 12 may fasten 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 ​​(set values) of the shooting parameters of the shooting device 32 according to the traffic conditions of the road (step S102). The shooting parameters may include, for example, at least one of aperture value, shutter speed, ISO sensitivity, sharpness, and contrast. Here, the control unit 12 may, for example, increase the exposure (amount of light captured by the camera) by adjusting the set value of at least one of the aperture value (F number), shutter speed, and ISO (International Organization for Standardization) sensitivity if the number of vehicles 50 traveling on the road is below a threshold, for example, at night when it is relatively dark. This is because, when the number of vehicles 50 traveling on the road is below a threshold, it is considered that the degree to which the subject is illuminated by the headlights of each vehicle 50 is relatively low. The ISO sensitivity may be, for example, a guideline value for amplifying the signal at the image sensor of a digital camera.

[0042] Furthermore, the control unit 12 may increase the aperture value setting if, for example, the number of vehicles 50 traveling on the road exceeds a threshold. This widens the range of focus, potentially increasing the accuracy of subject detection in areas near the edges of the image. Note that a larger aperture value means less light is taken in, resulting in a wider range of focus.

[0043] Furthermore, the control unit 12 may increase (strengthen) the sharpness (contour enhancement) setting if, for example, a specific type of vehicle 50 (e.g., a dump truck, a vehicle for the elderly, etc.) is present. This may increase the detection accuracy of types of vehicles that are relatively prone to accidents or that cause relatively large damage in the event of an accident.

[0044] Furthermore, the control unit 12 may increase the shutter speed setting based, for example, on at least one of the representative value (mean, mode, or median) and the maximum value of the travel speed of each vehicle 50 traveling on the road. This may, for example, reduce blur and thus increase the accuracy of subject detection.

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

[0046] Furthermore, the control unit 12 may determine the shutter speed setting based, for example, on the color of the traffic signal 30 on the road. In this case, the control unit 12 may increase the shutter speed setting, for example, if the light is green, because it is assumed that a vehicle 50 is moving. This may increase the accuracy of subject detection, for example, by reducing blur.

[0047] (Example of determining the load on the traffic signal base station 31) Furthermore, the control unit 12 may determine the values ​​of parameters related to the imaging device 32 in accordance with, for example, the traffic conditions and the load on the traffic signal base station 31 installed in association with the imaging device 32. In this case, the load on the traffic signal base station 31 may include at least one of the number of user terminals (e.g., vehicles 50 and terminals 60) located at the traffic signal base station 31, the amount of data for uplink communication to the traffic signal base station 31, and the amount of data for downlink communication from the traffic signal base station 31. Note that a user terminal being located at the traffic signal base station 31 means, for example, that the user terminal registers its location with the traffic signal base station 31 in order to perform uplink or downlink communication via the traffic signal base station 31.

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

[0049] Furthermore, if the load on the traffic signal base station 31 exceeds a threshold, the control unit 12 may perform object detection using a second trained model that can perform object detection faster (with less computation) than the first trained model. Also, if the load on the traffic signal base station 31 exceeds a threshold, the control unit 12 may determine parameter values ​​according to the traffic conditions based on a table for the second trained model or the like.

[0050] (An example of a decision made based on traffic conditions on nearby roads) The control unit 12 may also determine parameter values ​​in accordance with the traffic conditions of nearby roads. In this case, the acquisition unit 11 may acquire information indicating the traffic conditions on the road where the camera 32 is installed, and information indicating the traffic conditions at intersections adjacent to the intersection on that road. The control unit 12 may then determine the parameters related to the camera 32's photography in accordance with the traffic conditions on that road and the traffic conditions at intersections adjacent to the intersection on that road. This allows the control unit 12 to set parameters in the camera 32, for example, according to the number of vehicles 50 passing through the intersection ahead and their speed, before the vehicles 50 arrive on that road.

[0051] The control unit 12 may refer to the parameter setting table 501 in Figure 5 to identify the setting values ​​for each parameter according to traffic conditions, etc. In the example in Figure 5, the parameter setting table 501 records the setting values ​​for each parameter in association with combinations of the camera ID, traffic conditions, and the load of the traffic signal base station 31. The camera ID is the identification information of the camera 32. Traffic conditions may be classified (staged, grouped) according to the range of values ​​of each item included in the information indicating traffic conditions. In this case, for example, they may be divided into three classes: many, normal, and few, according to the number of vehicles 50 traveling on the road. The information in the parameter setting table 501 may be pre-set by an operator (administrator), etc.

[0052] Furthermore, the information in the parameter setting table 501 may be recorded by the control unit 12. In this case, the control unit 12 may use AI to detect objects for each parameter based on each image captured with each setting value for each shooting device 32 and for each traffic condition, etc. The control unit 12 may then record the set of setting values ​​that yield the highest accuracy in object detection in each image in the parameter setting table 501, associating it with the traffic condition, etc. This allows for the determination of appropriate setting values ​​for each traffic condition, etc., depending, for example, on the field of view of the shooting device 32, the background, etc.

[0053] The control unit 12 may, for example, determine the parameter value to set for the camera 32 based on the detection accuracy of a moving object using an image captured by the camera 32 with a first parameter value and the detection accuracy of a moving object using an image captured with a second parameter value. In this case, the control unit 12 may, for example, determine the detection accuracy of a vehicle 50, etc., using an image captured by the camera 32 with a first parameter value when the road is in a specific traffic condition. The control unit 12 may also, for example, determine the detection accuracy of a vehicle 50, etc., using an image captured by the camera 32 with a second parameter value when the road is in a specific traffic condition. The control unit 12 may, for example, determine the parameter value with higher detection accuracy among the first parameter value and the second parameter value as the parameter value to set for a specific traffic condition. The control unit 12 may also use, for example, the confidence (likelihood) value calculated as the likelihood of a vehicle 50 being detected by deep learning, etc., as a value indicating detection accuracy. Furthermore, the control unit 12 may determine the parameter values ​​to be set based on the detection accuracy of the moving object using images captured with three or more parameter values, rather than being limited to just the first and second parameter values.

[0054] Next, the control unit 12 of the information processing device 10 instructs the imaging device 32 to set the value of the identified parameter (step S103). At this point, the control unit 12 may also send a command to the imaging device 32 to set the value of the parameter.

[0055] Next, the imaging device 32 captures an image with the set parameter values ​​(step S104). Subsequently, the imaging device 32 transmits the captured 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 object, etc., using AI such as deep learning. Then, the control unit 12 may determine, for example, traffic conditions, etc., based on the detection results.

[0057] <Variation> The control unit 12 may, for example, control the values ​​of the parameters related to shooting according to information indicating road traffic conditions and information indicating at least one of the time of day and weather conditions. In this case, for example, the control unit 12 may control the parameter values ​​to increase the exposure time when it is nighttime, cloudy or rainy, and there are few vehicles on the road, because the road is dark.

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

[0059] Each functional unit of the information processing device 10 (for example, the control unit 12) may be implemented by cloud computing, which is composed of one or more computers. Alternatively, the information processing device 10 and the monitoring server 20 may be configured as a single server. Such information processing devices 10 are also included as examples of the "information processing device" in this disclosure.

[0060] It should be noted that the present invention is not limited to the embodiments described above, and can be modified as appropriate without departing from the spirit of the invention.

[0061] Some or all of the above embodiments may also be described as follows, but are not limited to the following: (Note 1) An acquisition unit that acquires information indicating the traffic conditions of a road where a camera that takes images to detect moving objects is installed, A control unit controls the values ​​of parameters related to the shooting device in accordance with the traffic condition information acquired by the acquisition unit, An information processing device having (Note 2) The information indicating the traffic conditions includes the number of vehicles traveling on the road. The information processing device described in Appendix 1. (Note 3) The information indicating the traffic conditions includes the type of vehicle traveling on the road. The information processing device described in Appendix 1 or 2. (Note 4) The information indicating the traffic conditions includes the speed of vehicles traveling on the road. The information processing device described in Appendix 1 or 2. (Note 5) The information indicating the traffic conditions includes the color of the vehicles traveling on the road. The information processing device described in Appendix 1 or 2. (Note 6) The information indicating the traffic conditions includes the status of the traffic signals on the road. The information processing device described in Appendix 1 or 2. (Note 7) The acquisition unit acquires information indicating the traffic conditions of the road and information indicating the traffic conditions at an intersection adjacent to the intersection of the road. The control unit controls the parameters related to the imaging device in accordance with the traffic conditions on the road and the traffic conditions at intersections adjacent to the intersection of the road. The information processing device described in Appendix 1 or 2. (Note 8) The control unit determines the value of the parameter to be set for the specific traffic condition, based on the detection accuracy of the moving object using an image captured by the camera with a first parameter value when the road is in a specific traffic condition, and the detection accuracy of the moving object using an image captured by the camera with a second parameter value when the road is in the specific traffic condition. The information processing device described in Appendix 1 or 2. (Note 9) The aforementioned parameters include at least one of aperture value, shutter speed, ISO sensitivity, sharpness, and contrast. The information processing device described in Appendix 1 or 2. (Note 10) The control unit controls the values ​​of the parameters according to the traffic conditions and the load of the base station installed in association with the imaging device. The information processing device described in Appendix 1 or 2. (Note 11) The load on the base station includes at least one of the number of user terminals located at the base station, the amount of data from uplink communications to the base station, and the amount of data from downlink communications to the base station. The information processing device described in Appendix 10. (Note 12) Information is obtained showing the traffic conditions on the road where a camera that takes images to detect moving objects is installed. The values ​​of the parameters related to the shooting of the shooting device are controlled according to the traffic conditions. Information processing methods. (Note 13) Information is obtained showing the traffic conditions on the road where a camera that takes images to detect moving objects is installed. The values ​​of the parameters related to the shooting of the shooting device are controlled according to the traffic conditions. A non-temporary, computer-readable medium containing a program that causes a computer to execute a process. [Explanation of symbols]

[0062] 1. Information Processing System 10 Information Processing Devices 11 Acquisition Department 12 Control Unit 20 Monitoring Servers 30 Traffic lights 31 Traffic signal base station 32 Imaging device 33 Signal control device 50 vehicles 60 devices

Claims

1. An acquisition unit that acquires information indicating the traffic conditions of a road where a camera that takes images to detect moving objects is installed, A control unit controls the values ​​of parameters related to the shooting device in accordance with the traffic condition information acquired by the acquisition unit, It has, The information indicating the traffic conditions includes the number of vehicles traveling on the road, as determined by the information processing device.

2. The information indicating the traffic conditions includes the speed of vehicles traveling on the road, The information processing apparatus according to claim 1.

3. The control unit determines the value of the parameter to be set in the case of the specific traffic condition, based on the detection accuracy of the moving object using an image captured by the camera with a first parameter value when the road is in a specific traffic condition, and the detection accuracy of the moving object using an image captured by the camera with a second parameter value when the road is in the specific traffic condition. The information processing apparatus according to claim 1.

4. An acquisition unit that acquires information indicating the traffic conditions of a road on which an imaging device for capturing images for detecting moving objects is installed, A control unit controls the values ​​of parameters related to the shooting device in accordance with the traffic condition information acquired by the acquisition unit, It has, The information indicating the traffic conditions includes the type of vehicle traveling on the road, and is an information processing device.

5. An acquisition unit that acquires information indicating the traffic conditions of a road on which an imaging device for capturing images for detecting moving objects is installed, A control unit controls the values ​​of parameters related to the shooting device in accordance with the traffic condition information acquired by the acquisition unit, It has, The information indicating the traffic conditions includes the status of the traffic signals on the road, as well as the status of the lights on the traffic signals on the road, as an information processing device.

6. The information processing device according to claim 5, wherein the information indicating traffic conditions includes the speed of vehicles traveling on the road.

7. The control unit, when the road is in a specific traffic condition, uses the imaging device to capture the first parameter The detection accuracy of the moving object based on the image captured at the - value, and the road under the specific traffic conditions. In that case, the image of the moving body captured by the imaging device with the second parameter value is Based on the detection accuracy, the value of the parameter to be set for the specific traffic situation is determined. The information processing apparatus according to claim 5.

8. An acquisition unit that acquires information indicating the traffic conditions of a road on which an imaging device for capturing images for detecting moving objects is installed, A control unit controls the values ​​of parameters related to the shooting device in accordance with the traffic condition information acquired by the acquisition unit, It has, The acquisition unit acquires information indicating the traffic conditions of the road and information indicating the traffic conditions at an intersection adjacent to the intersection of the road. The control unit monitors the traffic conditions on the road and the traffic conditions at intersections adjacent to the intersections on the road. An information processing device that controls the parameters related to imaging of the imaging device in accordance with the circumstances.

9. Information is obtained showing the traffic conditions on the road where a camera that takes images to detect moving objects is installed. Depending on the traffic conditions, the values ​​of the parameters related to the shooting of the shooting device are controlled. Information processing method for the aforementioned traffic conditions, including the number of vehicles traveling on the aforementioned road.

10. Information indicating the traffic conditions on a road where a camera that takes images to detect moving objects is installed is acquired. Depending on the traffic conditions, the values ​​of the parameters related to the shooting of the shooting device are controlled. An information processing method in which the information indicating the traffic conditions includes the status of the lights on the traffic signals of the road.

11. Information indicating the traffic conditions of a road where a camera that takes images for detecting moving objects is installed is obtained, Depending on the traffic conditions, the values ​​of the parameters related to the shooting of the shooting device are controlled. A program that causes a computer to perform a process that includes the number of vehicles traveling on the road in the information indicating the traffic conditions.

12. Acquire information indicating the traffic conditions of a road on which a camera that takes images for detecting moving objects is installed, Depending on the traffic conditions, the values ​​of the parameters related to the shooting of the shooting device are controlled. A program that causes a computer to perform a process that includes the status of the traffic signals on the road in the information indicating the traffic conditions.

Images