Image processing system, image processing method, and image processing program

Abstract

The purpose of the present disclosure is to provide an image processing system, an image processing method, and an image processing program with which it is possible to reduce a processing load on a device accompanying the detection of an animal and use amount of resources. This image processing system (1) includes: a traffic amount calculation means (101) which uses captured images of roads to calculate at least one among the traffic amount of people and the traffic amount of vehicles; a necessity estimation means (102) which, on the basis of the calculated traffic amounts, estimates necessity that indicates the degree of necessity for executing an animal detection process for detecting an animal using the captured images; an execution determination means (103) which, on the basis of the estimated necessity, determines whether to execute an animal detection process using the captured images; and an animal detection processing means (104) which, when it has been determined to execute the animal detection process, uses the captured images to execute the animal detection process.

Description

[Technical Field]

[0001] The present disclosure relates to an image processing system, an image processing method, and an image processing program. [Background technology]

[0002] In recent years, there has been an increase in the number of wild animals, such as bears, deer, and wild boars, spotted in human settlements. In response to this, various technologies have been proposed for detecting animals on or near roads. As an example of such technology, Patent Document 1 discloses an image processing system that acquires images of a road on which a vehicle is traveling from a plurality of different directions and detects targets, such as people and animals, from the images. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-055177 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when building a system for detecting animals in various locations using the image processing system disclosed in Patent Document 1, image analysis processing for detecting animals is performed on a large number of captured images generated by a large number of image capturing devices installed in various locations. As a result, the amount of data to be processed for detecting animals becomes enormous, posing a problem of increased processing load and resource usage of the device, such as memory, CPU, and network resources.

[0005] In view of the above-mentioned problems, an object of the present disclosure is to provide an image processing system, an image processing method, and an image processing program that can reduce the processing load and resource usage of an apparatus associated with animal detection. [Means for solving the problem]

[0006] An image processing system according to an exemplary embodiment includes: a traffic volume calculation means for calculating at least one of a traffic volume of people and a traffic volume of vehicles using a captured image of a road; a necessity estimation means for estimating a degree of necessity indicating a degree of necessity for executing an animal detection process for detecting animals using captured images based on the calculated traffic volume; execution decision means for deciding whether or not to execute an animal detection process using the captured image based on the estimated degree of necessity; and animal detection processing means for executing the animal detection process using the captured image when it is determined that the animal detection process should be executed.

[0007] An image processing method according to an exemplary embodiment includes: An image processing device that processes images, Calculating at least one of the volume of pedestrian traffic and the volume of vehicular traffic using the captured image of the road; Based on the calculated traffic volume, a necessity level indicating a degree of necessity for performing an animal detection process for detecting animals using the captured image is estimated; determining whether to perform an animal detection process using the captured image based on the estimated degree of necessity; If it is determined that the animal detection process is to be performed, the animal detection process is performed using the captured image.

[0008] An image processing program according to an exemplary embodiment includes: For computers, calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a step of estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to perform an animal detection process using the captured image based on the estimated degree of necessity; If it is determined that the animal detection process is to be performed, a step of performing the animal detection process using the captured image is executed.

[0009] An image processing system according to another exemplary embodiment includes: a traffic volume calculation means for calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a necessity estimation means for estimating a degree of necessity indicating a degree of necessity for executing an animal detection process for detecting animals using captured images based on the calculated traffic volume; execution decision means for deciding whether or not to execute an animal detection process using the captured image based on the estimated degree of necessity; Including, When it is determined that the animal detection process is to be executed, the execution determining means causes the device that executes the animal detection process to execute the animal detection process using the captured image.

[0010] An image processing method according to another exemplary embodiment includes: An image processing device that processes images, Calculating at least one of the volume of pedestrian traffic and the volume of vehicular traffic using the captured image of the road; Based on the calculated traffic volume, a necessity level indicating a degree of necessity for performing an animal detection process for detecting animals using the captured image is estimated; determining whether to perform an animal detection process using the captured image based on the estimated degree of necessity; If it is determined that the animal detection process is to be performed, the device that performs the animal detection process is caused to perform the animal detection process using the captured image.

[0011] An image processing program according to another exemplary embodiment includes: For computers, calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a step of estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to perform an animal detection process using the captured image based on the estimated degree of necessity; If it is determined that the animal detection process is to be performed, the step of causing the device that performs the animal detection process to perform the animal detection process using the captured image is executed. [Effects of the Invention]

[0012] The present disclosure makes it possible to provide an image processing system, an image processing method, and an image processing program that can reduce the processing load on an apparatus and the amount of resources used when detecting animals. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram illustrating an image processing system according to a first exemplary embodiment. [Figure 2] 1 is a diagram illustrating a configuration of an image processing apparatus according to a first embodiment. [Figure 3] FIG. 10 is a diagram showing an example of a necessity estimation table for estimating the necessity using the traffic volume of people and the traffic volume of vehicles. [Figure 4] FIG. 10 is a diagram showing an example of a necessity degree estimation table for estimating the necessity degree using the traffic volume of people. [Figure 5] FIG. 10 is a diagram showing an example of a necessity degree estimation table for estimating the necessity degree using the traffic volume of vehicles. [Figure 6] FIG. 10 is a diagram showing another example of a necessity degree estimation table for estimating the necessity degree using the traffic volume of people and the traffic volume of vehicles. [Figure 7] FIG. 10 is a diagram showing another example of a necessity degree estimation table for estimating the necessity degree using the traffic volume of people. [Figure 8] FIG. 10 is a diagram showing another example of a necessity degree estimation table for estimating the necessity degree using vehicle traffic volume. [Figure 9] FIG. 10 is a diagram showing another example of a necessity degree estimation table for estimating the necessity degree using the traffic volume of people and the traffic volume of vehicles. [Figure 10] FIG. 10 is a diagram showing another example of a necessity degree estimation table for estimating the necessity degree using the traffic volume of people. [Figure 11] FIG. 10 is a diagram showing another example of a necessity degree estimation table for estimating the necessity degree using vehicle traffic volume. [Figure 12] 5 is a flowchart illustrating an example of processing executed by the image processing apparatus according to the first embodiment. [Figure 13]5 is a flowchart illustrating an example of processing executed by the image processing apparatus according to the first embodiment. [Figure 14] 1 is a block diagram showing main components of an image processing system according to a first embodiment. [Figure 15] FIG. 10 is a diagram illustrating a configuration of an image processing device according to an exemplary third embodiment. [Figure 16] FIG. 10 is a block diagram showing main components of an image processing system according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] First Embodiment

[0023] Exemplary embodiments will be described below with reference to the drawings. Fig. 1 is a diagram showing an image processing system 1 according to an exemplary first embodiment. The image processing system 1 includes an image processing device 10 and one or more image capturing devices 20.

[0015] The photographing device 20 is a device that photographs roads. The photographing device 20 can be installed directly above or near the road. The photographing device 20 constantly photographs the road to be photographed to generate photographed images, and provides the photographed images to the image processing device 10 via a network 30. The network 30 can be constructed wirelessly and / or wired. The network 30 can include various networks such as a LAN (Local Area Network) and / or a WAN (Wide Area Network).

[0016] The image processing device 10 is a device that processes captured images generated by the imaging device 20. A specific example of the image processing device 10 is a computer such as a server in a client-server system. FIG. 2 is a diagram showing the configuration of the image processing device 10 according to the first embodiment. The image processing device 10 includes a processor 11 capable of executing various programs, a communication interface (I / F) 12, and a storage device 13. Specific examples of the processor 11 include various processors such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit).

[0017] The image processing device 10 receives the captured image transmitted by the photographing device 20 via the communication interface 12. Upon receiving the captured image, the image processing device 10 stores the captured image in the storage device 13. In addition to the captured image, the storage device 13 stores various information to be processed by the processor 11, such as an image processing program 100 and a data table.

[0018] The processor 11 executes the image processing method according to the first embodiment by reading and executing an image processing program 100 from the storage device 13. The image processing program 100 includes a traffic volume calculation unit 101, a necessity estimation unit 102, an execution decision unit 103, and an animal detection processing unit 104. Note that the functions of the image processing program 100 may be realized by an integrated circuit such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The processor, FPGA, ASIC, and other integrated circuits correspond to computers.

[0019] The traffic volume calculation unit 101 is a program that calculates at least one of the traffic volume of people and the traffic volume of vehicles using the captured image generated by the image capture device 20. For example, the traffic volume calculation unit 101 can calculate the traffic volume of people and / or the traffic volume of vehicles by detecting people and / or vehicles present in the captured image and counting the number of people and / or vehicles. The traffic volume calculation unit 101 may also calculate the traffic volume of vehicles based on traffic information received from an external road traffic information system. Furthermore, the traffic volume calculation unit 101 may calculate the traffic volume of people and / or the traffic volume of vehicles based on information from sensors other than cameras installed on the road.

[0020] The necessity estimation unit 102 is a program that estimates the degree of necessity to execute an animal detection process for detecting animals using a captured image based on the traffic volume of people and / or vehicles calculated by the traffic volume calculation unit 101. The degree of necessity is an index that indicates the degree of necessity to execute an animal detection process using the captured image. In this embodiment, three types of indexes, "high," "medium," and "low," are used as the degree of necessity. In other embodiments, two types of degree of necessity or four or more types of degree of necessity may be used. Furthermore, any numerical value may be used as the degree of necessity.

[0021] Specifically, the necessity estimation unit 102 can identify the necessity corresponding to the human traffic volume and the vehicular traffic volume calculated by the traffic volume calculation unit 101, using the necessity determined based on the human traffic volume and the vehicular traffic volume, the likelihood of an animal that has the target shooting area as its activity range, and the degree of influence the animal has on humans and vehicles. The necessity can be registered in a necessity estimation table, which is a data table. A necessity estimation table can be prepared for each type of animal.

[0022] The necessity estimation tables shown in Figures 3 to 5 are examples of necessity estimation tables in which degrees of necessity determined assuming bears are registered. Figure 3 shows an example of a necessity estimation table for estimating degrees of necessity using the traffic volume of people and the traffic volume of vehicles. The necessity estimation table shown in Figure 3 registers degrees of necessity determined based on the traffic volume of people and the traffic volume of vehicles, the possibility of animals appearing, and the degree of impact that animals have on people and the degree of impact that animals have on vehicles.

[0023] As shown in the necessity estimation table in Figure 3, since bears are wild animals, the degree of impact on people can be set to "large" or "medium." On the other hand, although bears are unlikely to suddenly jump out onto the road, the damage caused by a collision with a vehicle is significant, so the degree of impact on vehicles can be set to "medium" or "small."

[0024] Furthermore, the necessity estimation unit 102 can specify the necessity corresponding to the human traffic volume calculated by the traffic volume calculation unit 101, using the necessity determined based on the human traffic volume, the possibility of animal appearance, and the degree of influence that animals have on people. Fig. 4 shows an example of a necessity estimation table for estimating the necessity using the human traffic volume. The necessity estimation table shown in Fig. 4 has registered therein the necessity determined based on the human traffic volume, the possibility of animal appearance, and the degree of influence that animals have on people.

[0025] Furthermore, the necessity estimation unit 102 can specify the necessity corresponding to the vehicle traffic volume calculated by the traffic volume calculation unit 101, using the necessity determined based on the vehicle traffic volume, the possibility of an animal appearing, and the degree of influence that the animal has on the vehicle. Fig. 5 shows an example of a necessity estimation table for estimating the necessity using the vehicle traffic volume. The necessity estimation table shown in Fig. 5 has registered therein the necessity determined based on the vehicle traffic volume, the possibility of an animal appearing, and the degree of influence that the animal has on the vehicle.

[0026] The necessity estimation tables shown in Figs. 6 to 8 are examples of necessity estimation tables in which degrees of necessity determined assuming deer are registered. Fig. 6 is an example of a necessity estimation table for estimating degrees of necessity using pedestrian traffic volume and vehicular traffic volume. Fig. 7 is an example of a necessity estimation table for estimating degrees of necessity using pedestrian traffic volume. Fig. 8 is an example of a necessity estimation table for estimating degrees of necessity using vehicular traffic volume.

[0027] As shown in the necessity estimation table in Figure 6, in the case of deer, there is a slight possibility that they may collide with people and cause damage, so the degree of impact on people can be set to "medium" or "small." On the other hand, there is a possibility that deer may suddenly jump out onto the road, and damage may be great if they collide with a vehicle, so the degree of impact on vehicles can be set to "large" or "medium."

[0028] The necessity estimation tables shown in Figs. 9 to 11 are examples of necessity estimation tables in which degrees of necessity determined assuming wild boars are registered. Fig. 9 is an example of a necessity estimation table for estimating degrees of necessity using pedestrian traffic volume and vehicular traffic volume. Fig. 10 is an example of a necessity estimation table for estimating degrees of necessity using pedestrian traffic volume. Fig. 11 is an example of a necessity estimation table for estimating degrees of necessity using vehicular traffic volume.

[0029] As shown in the necessity estimation table of FIG. 9, in the case of wild boars, there is a slight possibility that they may collide with people and cause damage, so the degree of impact on people can be set to "medium" or "small." boar Although there is a possibility that the vehicle may suddenly jump out onto the road, the damage caused when it collides with the vehicle is small, and therefore the degree of impact on the vehicle may be rated as "medium" or "small."

[0030] The necessity estimation unit 102 can use a necessity estimation table corresponding to an animal that may appear in the area where the image capture device 20 is installed. For example, when processing an image captured by the image capture device 20 installed in an area where a bear may appear, the necessity estimation unit 102 can estimate the degree of necessity using a necessity estimation table corresponding to a bear (FIGS. 3 to 5).

[0031] Furthermore, when multiple moving objects other than people and vehicles exist in the captured image, the necessity estimation unit 102 can estimate the necessity for each moving object. These moving objects are usually highly likely to be animals. Therefore, the necessity estimation unit 102 can estimate the necessity for each of the multiple animals present in the captured image.

[0032] In this embodiment, the necessity estimation unit 102 estimates the degree of necessity using the degree of necessity registered in advance in the necessity estimation table, but in other embodiments, the necessity degree estimation unit 102 may calculate the degree of necessity using the possibility of an animal appearing, the degree of influence that an animal has on people, and / or the degree of influence that an animal has on a vehicle, without registering the degree of necessity in a data table in advance.

[0033] For example, the degree of necessity may be the average of the possibility of an animal appearing, the degree of influence that an animal has on people, and the degree of influence that an animal has on vehicles. The degree of necessity may also be the maximum value of the possibility of an animal appearing, the degree of influence that an animal has on people, and the degree of influence that an animal has on vehicles. In this case, for example, if there is even one "high" influence level, the degree of necessity will be "high." Furthermore, the degree of necessity may be the sum of the possibility of an animal appearing, the degree of influence that an animal has on people, and the degree of influence that an animal has on vehicles, weighted in a predetermined manner.

[0034] The execution decision unit 103 is a program that decides whether to execute animal detection processing using a captured image based on the degree of necessity estimated by the necessity estimation unit 102. The execution decision unit 103 can decide to execute the animal detection processing when the degree of necessity estimated by the necessity estimation unit 102 is a specific degree of necessity. For example, the execution decision unit 103 can decide to execute the animal detection processing when the degree of necessity estimated by the necessity estimation unit 102 is "high." In an embodiment in which the degree of necessity is defined by a numerical value, the execution decision unit 103 can decide to execute the animal detection processing when the degree of necessity estimated by the necessity estimation unit 102 is equal to or greater than a predetermined threshold.

[0035] The animal detection processing unit 104 is a program that executes animal detection processing using a captured image. The animal detection processing unit 104 executes the animal detection processing only when the execution decision unit 103 decides to execute the animal detection processing. The animal detection processing unit 104 can analyze the captured image using various image analysis algorithms that can detect animals and output the analysis results. The animal detection processing unit 104 can use image analysis algorithms for each type of animal that may appear in the capture target area.

[0036] 12 and 13 are flowcharts showing an example of processing executed by the image processing device 10. In step S1, the traffic volume calculation unit 101 selects at least one of the captured images received by the image processing device 10, and calculates the traffic volume of people using the selected captured image. In step S2, the traffic volume calculation unit 101 calculates the traffic volume of vehicles using the selected captured image.

[0037] In step S3, the necessity estimation unit 102 determines whether the pedestrian traffic volume and vehicular traffic volume calculated by the traffic volume calculation unit 101 are both zero. If both the pedestrian traffic volume and vehicular traffic volume are zero (YES), the process returns to step S1. Thereafter, the processes shown in Figs. 12 and 13 are executed for another captured image.

[0038] On the other hand, if the pedestrian traffic volume and vehicular traffic volume are not zero (NO), the process branches to step S4. In step S4, the necessity estimation unit 102 determines whether or not both the pedestrian traffic volume and vehicular traffic volume calculated by the traffic volume calculation unit 101 are zero. If both the pedestrian traffic volume and vehicular traffic volume are not zero (YES), in step S5, the necessity estimation unit 102 refers to a necessity estimation table for estimating necessity using the pedestrian traffic volume and vehicular traffic volume, and identifies the necessity corresponding to the pedestrian traffic volume and vehicular traffic volume calculated by the traffic volume calculation unit 101.

[0039] On the other hand, if it is determined in step S4 that either the pedestrian traffic volume or the vehicular traffic volume is zero (NO), the process branches to step S6. In step S6, the necessity estimation unit 102 determines whether the pedestrian traffic volume calculated by the traffic volume calculation unit 101 is zero. If the pedestrian traffic volume is not zero (YES), in step S7, the necessity estimation unit 102 refers to a necessity estimation table for estimating necessity using pedestrian traffic volume, and identifies the necessity corresponding to the pedestrian traffic volume calculated by the traffic volume calculation unit 101.

[0040] On the other hand, if it is determined in step S6 that the pedestrian traffic volume is zero (NO), that is, if the vehicle traffic volume is not zero, in step S8, the necessity estimation unit 102 refers to a necessity estimation table for estimating the necessity using the vehicle traffic volume, and identifies the necessity corresponding to the vehicle traffic volume calculated by the traffic volume calculation unit 101.

[0041] In step S9, the execution decision unit 103 decides whether or not to execute the animal detection process using the photographed image selected in step S1, based on the degree of necessity determined by the necessity estimation unit 102. If it is decided not to execute the animal detection process (NO), the process returns to step S1. Thereafter, the processes shown in Figs. 12 and 13 are executed for other photographed images.

[0042] On the other hand, if it is determined that the animal detection process is to be executed (YES), the process branches to step S10. In step S10, the animal detection processing unit 104 executes the animal detection process using the photographed image selected in step S1, and the process returns to step S1. Thereafter, the processes shown in Figs. 12 and 13 are executed for other photographed images.

[0043] 14 is a block diagram showing the main components of the image processing system 1 according to the first embodiment. The image processing system 1 includes a traffic volume calculation unit 101, a necessity estimation unit 102, an execution decision unit 103, and an animal detection processing unit 104. The traffic volume calculation unit 101, the necessity estimation unit 102, the execution decision unit 103, and the animal detection processing unit 104 can be implemented in a single image processing device that functions as a server in a client-server system. Alternatively, the traffic volume calculation unit 101, the necessity estimation unit 102, the execution decision unit 103, and the animal detection processing unit 104 can each be implemented in an individual image processing device that functions as a server. These image processing devices correspond to the image processing system 1.

[0044] The traffic volume calculation unit 101 calculates at least one of the volume of pedestrian traffic and the volume of vehicular traffic using the captured images. The necessity estimation unit 102 estimates a degree of necessity indicating the degree of necessity to execute an animal detection process that detects animals using the captured images, based on the calculated traffic volume. The execution decision unit 103 decides whether to execute the animal detection process using the captured images, based on the estimated degree of necessity. If it is decided to execute the animal detection process, the animal detection processing unit 104 executes the animal detection process using the captured images.

[0045] This allows the amount of data required for the animal detection process to be reduced, since the animal detection process is performed using the captured image only when it is determined that the animal detection process should be performed. As a result, the processing load on the image processing device 10 that performs the animal detection process can be reduced, and the amount of resource usage, such as the memory, CPU, and data bus, of the image processing device 10 required for the execution of the animal detection process can be reduced.

[0046] Furthermore, the necessity estimation unit 102 identifies the necessity corresponding to the calculated human traffic volume based on the probability of animal appearance and the degree of influence that animals have on people, which are predetermined in association with the human traffic volume. This makes it possible to estimate the necessity taking into account the human traffic volume, the probability of animal appearance, and the degree of influence that animals have on people.

[0047] Furthermore, the necessity estimation unit 102 identifies the necessity corresponding to the calculated vehicle traffic volume based on the probability of an animal appearing and the degree of impact that the animal has on the vehicle, which are predetermined in association with the vehicle traffic volume. This makes it possible to estimate the necessity taking into consideration the vehicle traffic volume, the probability of an animal appearing, and the degree of impact that the animal has on the vehicle.

[0048] Furthermore, the necessity estimation unit 102 identifies the necessity corresponding to the calculated pedestrian traffic volume and vehicular traffic volume based on the likelihood of an animal appearing, the degree of influence of the animal on people, and the degree of influence of the animal on vehicles, which are predetermined in association with the pedestrian traffic volume and vehicular traffic volume. This makes it possible to estimate the necessity taking into account the pedestrian traffic volume, vehicular traffic volume, the likelihood of an animal appearing, and the degree of influence of the animal on people and vehicles.

[0049] Furthermore, the determined degree of necessity may be determined according to the type of animal whose range of activity is the location where the photographing device that generated the photographed image is installed, thereby making it possible to estimate the degree of necessity according to the type of animal.

[0050] <Second embodiment> In the second exemplary embodiment, the necessity estimation unit 102 can determine the necessity based on the volume of human traffic or vehicle traffic and the characteristics of animals whose movement range is within the target area. For example, the personality of the animal can be used as the animal's characteristics.

[0051] For example, in the case of an animal with a timid personality, when the amount of human traffic is equal to or greater than a predetermined amount, the necessity degree estimation unit 102 can determine the degree of necessity as "none." The predetermined amount of traffic can be the minimum amount of human traffic at which the animal is unlikely to appear in the target shooting area. On the other hand, when the amount of human traffic is less than the predetermined amount, the necessity degree estimation unit 102 can determine the degree of necessity as "yes." This makes it possible to determine the degree of necessity based on the amount of human traffic and the characteristics of the animal.

[0052] Similarly, in the case of an animal with a timid personality, if the vehicle traffic volume is equal to or greater than a predetermined traffic volume, the necessity degree estimation unit 102 can determine the degree of necessity as "none." The predetermined traffic volume can be the minimum vehicle traffic volume at which the animal is unlikely to appear in the shooting target area. On the other hand, if the vehicle traffic volume is less than the predetermined traffic volume, the necessity degree estimation unit 102 can determine the degree of necessity as "yes." This makes it possible to determine the degree of necessity based on the vehicle traffic volume and the characteristics of the animal.

[0053] Furthermore, in the case of an animal with a ferocious personality, the necessity estimation unit 102 can determine the degree of necessity as "yes" only when there is a lot of human traffic. In other cases, the necessity estimation unit 102 can determine the degree of necessity as "no." This makes it possible to determine the degree of necessity based on the amount of human traffic and the characteristics of the animal.

[0054] If the degree of necessity is determined to be "not necessary," the execution decision unit 103 decides not to execute the animal detection process. On the other hand, if the degree of necessity is determined to be "necessary," the execution decision unit 103 decides to execute the animal detection process.

[0055] <Third embodiment> FIG. 15 is a block diagram illustrating an image processing apparatus according to a third exemplary embodiment. 4 3 is a diagram showing the configuration of an image processing device 40 according to the third embodiment. In the third embodiment, the image processing device 40 can be realized as an edge server in edge computing. Differences from the first and second embodiments will be described below.

[0056] The image processing program 100 includes a traffic volume calculation unit 101, a necessity estimation unit 102, and an execution decision unit 103. When the necessity estimation unit 102 decides to execute the animal detection processing, the execution decision unit 103 causes the device that executes the animal detection processing to execute the animal detection processing using the captured image. In the third embodiment, an image processing device separate from the image processing device 40 executes the animal detection processing using the captured image. For example, a computer such as a server in a client-server system can be used as the separate image processing device. Hereinafter, the image processing device 40 will be referred to as a first image processing device, and the separate image processing device will be referred to as a second image processing device.

[0057] The first image processing device transmits, via a network, to the second image processing device, an execution command for the animal detection process and the captured image to be used in the animal detection process. When the second image processing device receives the execution command for the animal detection process and the captured image from the first image processing device, the animal detection processing unit 104 of the second image processing device executes the animal detection process using the captured image.

[0058] As a result, the second image processing device executes the animal detection process using the captured image only when it is determined to execute the animal detection process, thereby reducing the amount of data involved in the animal detection process, thereby reducing the processing load on the second image processing device and reducing the amount of resource usage such as the memory, CPU, and data bus of the second image processing device.

[0059] Furthermore, only when it is determined that the animal detection process is to be executed, the first image processing device transmits an execution command for the animal detection process and the captured image to be used in the animal detection process to the second image processing device via the network, thereby reducing the usage of network resources such as network devices (hubs, routers, etc.) and communication paths (radio waves, network cables, etc.).

[0060] 16 is a block diagram showing main components of an image processing system 1 according to the third embodiment. The image processing system 1 includes a traffic volume calculation unit 101, a necessity estimation unit 102, and an execution decision unit 103. The traffic volume calculation unit 101, the necessity estimation unit 102, and the execution decision unit 103 can be implemented in a single image processing device that functions as an edge server. Alternatively, the traffic volume calculation unit 101, the necessity estimation unit 102, and the execution decision unit 103 can each be implemented in an individual image processing device that functions as an edge server. These image processing devices correspond to the image processing system 1.

[0061] In the above example, the image processing program 100 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 image processing program 100 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 technology, CD-ROM, digital versatile disk (DVD), Blu-ray® disk 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.

[0062] The present disclosure is not limited to the above-described embodiments, and can be modified as appropriate within the scope of the present disclosure.

[0063] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) a traffic volume calculation means for calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a necessity estimation means for estimating a degree of necessity indicating a degree of necessity for executing an animal detection process for detecting animals using the captured images based on the calculated traffic volume; an execution decision means for deciding whether or not to execute the animal detection process using the captured image based on the estimated degree of necessity; an animal detection processing means for executing the animal detection processing using the photographed image when it is determined that the animal detection processing is to be executed; an image processing system comprising: (Appendix 2) The image processing system described in Appendix 1, wherein the necessity estimation means identifies the necessity corresponding to the calculated pedestrian traffic volume based on the probability of the animal appearing and the degree of impact the animal has on people, which are predetermined in correspondence with the pedestrian traffic volume. (Appendix 3) The image processing system described in Appendix 1 or 2, wherein the necessity estimation means identifies the necessity corresponding to the calculated vehicle traffic volume based on the probability of the animal appearing and the degree of impact the animal has on the vehicle, which are predetermined in correspondence with the vehicle traffic volume. (Appendix 4) The image processing system described in any one of Appendices 1 to 3, wherein the necessity estimation means determines the necessity corresponding to the calculated pedestrian traffic volume and vehicular traffic volume based on the probability of the animal appearing and the degree of influence the animal has on people and the degree of influence the animal has on vehicles, which are predetermined in correspondence with the pedestrian traffic volume and the vehicular traffic volume. (Appendix 5) An image processing system according to any one of appendices 2 to 4, wherein the determined degree of necessity is determined according to the type of animal whose range of activity is the location where the photographing device that generated the photographed image is installed. (Appendix 6) The image processing system according to claim 1, wherein the necessity estimation means determines the necessity based on the traffic volume of people or the traffic volume of vehicles and the characteristics of the animals. (Appendix 7) An image processing device that processes images, Calculating at least one of the volume of pedestrian traffic and the volume of vehicular traffic using the captured image of the road; estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to execute the animal detection process using the captured image based on the estimated degree of necessity; an image processing method, wherein, when it is determined that the animal detection process is to be performed, the animal detection process is performed using the captured image; (Appendix 8) For computers, calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a step of estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to execute the animal detection process using the captured image based on the estimated degree of necessity; a step of executing the animal detection process using the captured image when it is determined that the animal detection process is to be executed; A non-transitory recording medium on which an image processing program is recorded. (Appendix 9) a traffic volume calculation means for calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a necessity estimation means for estimating a degree of necessity indicating a degree of necessity for executing an animal detection process for detecting animals using the captured images based on the calculated traffic volume; an execution decision means for deciding whether or not to execute the animal detection process using the captured image based on the estimated degree of necessity; Including, When the execution decision means decides to execute the animal detection process, the execution decision means causes a device that executes the animal detection process to execute the animal detection process using the captured image. (Appendix 10) The image processing system described in Appendix 9, wherein the necessity estimation means identifies the necessity corresponding to the calculated pedestrian traffic volume based on the probability of the animal appearing and the degree of impact the animal has on people, which are predetermined in correspondence with the pedestrian traffic volume. (Appendix 11) The image processing system described in Appendix 9 or 10, wherein the necessity estimation means identifies the necessity corresponding to the calculated vehicle traffic volume based on the probability of the animal appearing and the degree of impact the animal has on the vehicle, which are predetermined in correspondence with the vehicle traffic volume. (Appendix 12) The image processing system described in any one of Appendices 9 to 11, wherein the necessity estimation means identifies the necessity corresponding to the calculated pedestrian traffic volume and vehicular traffic volume based on the probability of the animal appearing and the degree of influence the animal has on people and the degree of influence the animal has on vehicles, which are predetermined in correspondence with the pedestrian traffic volume and the vehicular traffic volume. (Appendix 13) An image processing system according to any one of Appendices 10 to 12, wherein the determined degree of necessity is determined according to the type of animal whose range of activity is the location where the photographing device that generated the photographed image is installed. (Appendix 14) The image processing system according to claim 9, wherein the necessity estimation means determines the necessity based on the traffic volume of people or the traffic volume of vehicles and the characteristics of the animals. (Appendix 15) An image processing device that processes images, Calculating at least one of the volume of pedestrian traffic and the volume of vehicular traffic using the captured image of the road; estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to execute the animal detection process using the captured image based on the estimated degree of necessity; when it is determined that the animal detection process is to be performed, causing a device that is to perform the animal detection process to perform the animal detection process using the captured image; Image processing methods. (Appendix 16) For computers, calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a step of estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to execute the animal detection process using the captured image based on the estimated degree of necessity; a step of causing a device that executes the animal detection process to execute the animal detection process using the captured image when it is determined that the animal detection process is to be executed; A non-transitory recording medium on which an image processing program is recorded. [Explanation of symbols]

[0064] 1. Image processing system 10 Image processing device 11 processors 12 Communication Interface 13 Storage device 100 Image Processing Programs 101 Traffic calculation department 102 Necessity estimation part 103 Execution Decision Department 104 Animal detection processing unit 20 Imaging equipment 30 Network 40 Image processing device

Claims

1. a traffic volume calculation means for calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a necessity estimation means for estimating a degree of necessity indicating a degree of necessity for executing an animal detection process for detecting animals using the captured images based on the calculated traffic volume; an execution decision means for deciding whether or not to execute the animal detection process using the captured image based on the estimated degree of necessity; an animal detection processing means for executing the animal detection processing using the photographed image when it is determined that the animal detection processing is to be executed, and not executing the animal detection processing when it is determined that the animal detection processing is not to be executed; an image processing system comprising:

2. The image processing system according to claim 1, wherein the necessity estimation means identifies the degree of necessity corresponding to the calculated pedestrian traffic volume based on a predetermined degree of necessity corresponding to the pedestrian traffic volume, the possibility of the animal appearing, and the degree of impact the animal has on people.

3. 3. The image processing system according to claim 1, wherein the necessity estimation means identifies a degree of necessity corresponding to the calculated vehicle traffic volume based on predetermined degrees of necessity corresponding to the vehicle traffic volume, the possibility of the animal appearing, and the degree of impact the animal has on the vehicle.

4. The image processing system according to any one of claims 1 to 3, wherein the necessity estimation means identifies the degree of necessity corresponding to the calculated pedestrian traffic volume and vehicular traffic volume based on predetermined degrees of necessity corresponding to the pedestrian traffic volume, the vehicular traffic volume, the possibility of the animal appearing, the degree of influence the animal has on people, and the degree of influence the animal has on vehicles.

5. The image processing system according to any one of claims 2 to 4, wherein the determined degree of necessity is a degree of necessity determined according to the type of animal whose range of activity is the location where the photographing device that generated the photographed image is installed.

6. An image processing device that processes images, Calculating at least one of the volume of pedestrian traffic and the volume of vehicular traffic using the captured image of the road; estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to execute the animal detection process using the captured image based on the estimated degree of necessity; If it is determined that the animal detection process is to be performed, the animal detection process is performed using the photographed image; If it is determined that the animal detection process is not to be performed, the animal detection process is not performed. Image processing methods.

7. For computers, calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a step of estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to execute the animal detection process using the captured image based on the estimated degree of necessity; a step of executing the animal detection process using the captured image when it is determined that the animal detection process is to be executed; Execute If it is determined that the animal detection process is not to be executed, the animal detection process is not executed. Image processing program.

8. a traffic volume calculation means for calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a necessity estimation means for estimating a degree of necessity indicating a degree of necessity for executing an animal detection process for detecting animals using the captured images based on the calculated traffic volume; an execution decision means for deciding whether or not to execute the animal detection process using the captured image based on the estimated degree of necessity; Including, When the execution decision means decides to execute the animal detection process, it causes a device that executes the animal detection process to execute the animal detection process using the captured image, and when the execution decision means decides not to execute the animal detection process, it does not cause the device to execute the animal detection process. Image processing system.

9. An image processing device that processes images, Calculating at least one of the volume of pedestrian traffic and the volume of vehicular traffic using the captured image of the road; estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to execute the animal detection process using the captured image based on the estimated degree of necessity; when it is determined that the animal detection process is to be performed, causing a device that is to perform the animal detection process to perform the animal detection process using the captured image; If it is determined not to execute the animal detection process, the device is not caused to execute the animal detection process. Image processing methods.

10. For computers, calculating at least one of a traffic volume of people and a traffic volume of vehicles using a photographed image of a road; a step of estimating a degree of necessity indicating a degree of necessity to perform an animal detection process for detecting animals using the captured image based on the calculated traffic volume; determining whether to execute the animal detection process using the captured image based on the estimated degree of necessity; a step of causing a device that executes the animal detection process to execute the animal detection process using the captured image when it is determined that the animal detection process is to be executed; Execute If it is determined not to execute the animal detection process, the device is not caused to execute the animal detection process. Image processing program.

Images