Crime prevention device and crime prevention method

Abstract

To provide a crime prevention device and a crime prevention method capable of effectively preventing a crime by using a mobile body.SOLUTION: A pedestrian detection unit receives a captured image transmitted from a vehicle and detects a pedestrian from the captured image. A pedestrian attribute estimation unit estimates an attribute of the pedestrian from the captured image. A pedestrian progressing region estimation unit estimates a pedestrian progressing region where the pedestrian walks on the basis of the captured image. A traffic calculation unit calculates a traffic in the pedestrian progressing region. A risk level calculation unit calculates a risk level of the pedestrian being involved in a crime on the basis of the attribute of the pedestrian and the traffic in the pedestrian progressing region. A patrol commanding unit transmits a patrol command to command patrol in the pedestrian progressing region to a mobile body when the risk level is higher than a preset threshold value.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a crime prevention device and a crime prevention method. [Background technology]

[0002] Patent document 1 describes an autonomous driving system that acquires external information from multiple mobile bodies that patrol automatically based on operation commands, determines patrol commands for each area based on the information acquired by each of the mobile bodies that have moved through the same area, and generates operation commands according to the determined patrol policy for the area. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-117574 A Summary of the Invention [Problem to be solved by the invention]

[0004] The automated driving system described in Patent Document 1 obtains the number of people as external information, and determines a patrol policy so that areas with fewer people are patrolled more frequently than areas with more people. However, when determining the patrol policy, the system does not take into consideration people's attributes or the amount of vehicular traffic (hereinafter referred to as vehicular traffic). For this reason, it is not possible to detect situations where pedestrians are at high risk of becoming involved in crime, such as when people who are more likely to become involved in crime, such as children or elderly people, are walking in areas with less vehicular traffic, and to prioritize patrol such situations.

[0005] An object of the present invention is to provide a crime prevention device and a crime prevention method that can more effectively prevent crimes by utilizing a moving object. [Means for solving the problem]

[0006] The present invention provides a crime prevention device comprising a pedestrian detection unit that receives an image transmitted from a vehicle and detects pedestrians from the image, a pedestrian attribute estimation unit that estimates pedestrian attributes from the image, a pedestrian movement area estimation unit that estimates a pedestrian movement area in which the pedestrian will walk based on the image, a vehicle traffic volume calculation unit that calculates the volume of vehicle traffic within the pedestrian movement area, a risk calculation unit that calculates the risk of the pedestrian being involved in a crime based on the pedestrian attributes and the volume of vehicle traffic within the pedestrian movement area, and a patrol command unit that sends a patrol command to a mobile body to patrol the pedestrian movement area if the risk is higher than a preset threshold.

[0007] The present invention provides a crime prevention method that receives an image transmitted from a vehicle, detects pedestrians from the image, estimates the attributes of the pedestrians from the image, estimates a pedestrian movement area in which the pedestrians will walk based on the image, calculates the amount of vehicular traffic within the pedestrian movement area, calculates the risk of the pedestrian being involved in a crime based on the pedestrian attributes and the amount of vehicular traffic within the pedestrian movement area, and if the risk is higher than a preset threshold, sends a patrol command to command patrol of the pedestrian movement area. Effect of the Invention

[0008] According to the crime prevention device and crime prevention method of the present invention, crime can be prevented more effectively by utilizing a moving object. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of a crime prevention system 1 including a crime prevention device according to one embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram showing an example of the configuration of the crime prevention system 1 shown in FIG. [Diagram 3] FIG. 3 is a diagram for explaining the main contents stored in the position information database 221 provided in the storage device 22. As shown in FIG. [Figure 4] FIG. 4 is a diagram for explaining the main contents stored in the attribute information database 222 provided in the storage device 22. As shown in FIG. [Diagram 5] 2 is a diagram for explaining main storage contents of a risk value database 223 provided in a storage device 22. FIG. [Figure 6] FIG. 6 is a diagram for explaining the function of a crime prevention device according to one embodiment of the present invention. [Figure 7] FIG. 7 is a flowchart illustrating an example of the operation of the crime prevention device according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. In the description of the drawings, the same parts are given the same reference numerals and the description will be omitted.

[0011] [Crime Prevention System Overview] An overview of a crime prevention system 1 including a crime prevention device according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing a schematic configuration of the crime prevention system 1. The crime prevention system 1 according to this embodiment includes a vehicle 40 that travels on a road, a plurality of autonomous vehicles 50 that perform autonomous travel based on a given patrol command, and a server 20 that collects and manages information transmitted from the vehicle 40 and the autonomous vehicles 50 and issues patrol commands to the autonomous vehicles 50. In this embodiment, the crime prevention device is implemented as a CPU (Central Processing Unit) 23 of the server 20.

[0012] The server 20 and the vehicle 40, and the server 20 and the multiple autonomous vehicles 50 are connected to each other by a network 30. In this embodiment, the server 20, the vehicle 40, and the autonomous vehicles 50 are connected to the network 30 by a wireless communication method. Note that the crime prevention system 1 shown in FIG. 1 includes one vehicle 40 and two autonomous vehicles 50 as an example, but the number of vehicles 40 may be two or more, and the number of autonomous vehicles 50 may be three or more. The number of vehicles 40 and the number of autonomous vehicles 50 are not particularly limited. The multiple autonomous vehicles 50 may be, for example, vehicles that patrol roads according to a predetermined patrol route for crime prevention measures or the like, or vehicles that wait at a predetermined waiting place and start patrolling upon receiving a patrol command. Note that, instead of the multiple autonomous vehicles 50, a mobile body that can be remotely operated or automatically driven, such as a drone, may be used. In addition, the server 20 may be mounted on the vehicle 40 or the autonomous vehicle 50, or may be an integral configuration with the vehicle 40 or the autonomous vehicle 50.

[0013] The vehicle 40 acquires captured images while traveling. The vehicle 40 may further acquire crime prevention-related information, such as information regarding the brightness of lighting at night, while traveling. The captured images and various information acquired by the vehicle 40 are transmitted to the server 20. The vehicle 40 may be a manually operated vehicle driven by a driver, or may be an autonomous vehicle.

[0014] The server 20 detects pedestrians from the captured images transmitted from the vehicle 40, and calculates the risk of the detected pedestrian becoming involved in a crime. If the risk is higher than a preset value, the server 20 generates a patrol command to patrol within the pedestrian movement area of ​​the detected pedestrian, and transmits the patrol command to the autonomous vehicle 50. The autonomous vehicle 50 that receives the patrol command generates an operation plan according to the patrol command, and travels according to the operation plan.

[0015] [Structure of crime prevention system] Next, each component of the crime prevention system 1 will be described in detail with reference to FIG. 2. FIG. 2 is a block diagram that shows an example of the configuration of the crime prevention system 1 shown in FIG. 1. Note that, in FIG. 2, one vehicle 40 and one autonomous vehicle 50 are shown as an example, but in reality, there may be a plurality of each. The crime prevention system 1 according to this embodiment manages individual vehicle IDs for identifying each of the vehicles 40 and autonomous vehicles 50 so that each of the pre-registered vehicles 40 and autonomous vehicles 50 can be individually identified. The crime prevention system 1 may manage the ID of the information transmission device 400 mounted on each vehicle 40 as the vehicle ID of the vehicle 40, or may manage the ID of the autonomous vehicle control device 500 mounted on each autonomous vehicle 50 as the vehicle ID of the autonomous vehicle 50.

[0016] The server 20 is a device that manages information on the vehicle 40 and the multiple autonomous vehicles 50, and transmits patrol commands to each of the autonomous vehicles 50. The server 20 communicates with the vehicle 40 and the multiple autonomous vehicles 50 via a network 30. The server 20 includes a communication device 21, a storage device 22, a CPU 23, and a memory (not shown), and these components are electrically connected via a bus (not shown) or the like. There are no particular limitations on where the server 20 is installed, but for example, the server 20 is installed in a management center of a business that provides local crime prevention services using the autonomous vehicles 50.

[0017] In this embodiment, the server 20, the vehicle 40, and the autonomously driven vehicle 50 are connected to the network 30 via wireless communication.

[0018] The communication device 21 communicates with the vehicle 40 and the autonomously driven vehicle 50 via the network 30. The communication device 21 may be, for example, a device equipped with a mobile communication function such as 4G / LTE, or may be a device equipped with a wireless LAN communication function.

[0019] The storage device 22 stores various information and databases necessary for crime prevention services. The storage device 22 is a storage medium such as a hard disk drive (HDD). The storage device 22 includes, for example, a position information database 221, an attribute information database 222, and a risk value database 223. Details of the various databases will be described later with reference to Figs. 3 to 5.

[0020] The CPU 23 controls the server 20. The CPU 23 loads various programs stored in the storage device 22 or the like into a memory, and executes various commands included in the programs. The memory is a storage medium such as a ROM (Read Only Memory) or a RAM (Random Access Memory).

[0021] The CPU 23 includes, as examples of functional configurations, a position information management unit 231, a pedestrian detection unit 232, a pedestrian attribute estimation unit 233, a pedestrian travel area estimation unit 234, a vehicle traffic volume calculation unit 235, a risk level calculation unit 236, and a tour command unit 237. Details of each function of the CPU 23 will be described later with reference to FIG.

[0022] The vehicle 40 is equipped with an information transmission device 400 including a communication device 41, a camera 42, a storage device 43, and a GPS receiver 44.

[0023] The communication device 41 has a similar configuration to the communication device 21 , and communicates with the server 20 via the network 30 .

[0024] The camera 42 is an imaging device that captures an image of the outside of the vehicle 40. The camera 42 captures an image of a subject using an imaging element such as a CCD (Charge Coupled Device) image sensor or a CMOS (Complementary Metal Oxide Semiconductor) image sensor. The image obtained by capturing an image may be either a still image or a video image. The camera 42 is provided, for example, on the back side of the windshield of the vehicle and captures an image of at least the traveling direction (forward) of the vehicle 40, but the direction in which the camera 42 captures an image is not limited to a specific direction, and the camera 42 may capture an image in all directions of the vehicle 40. The captured image captured by the camera 42 is stored in the storage device 43 and transmitted to the server 20 at any timing. The storage device 43 stores various information. The storage device 43 is any storage medium such as a RAM, a magnetic disk, or a flash memory.

[0025] The position information of the vehicle 40 acquired by the GPS receiver 44 is transmitted to the server 20 at any timing. The GPS receiver 44 receives signals from a plurality of artificial satellites, and supplies the received signals to a CPU (not shown) mounted in the GPS receiver to calculate the position information of the vehicle 40 on the ground. Note that "GPS" is an abbreviation for "Global Positioning System."

[0026] The autonomous vehicle 50 is a vehicle that autonomously travels based on a patrol command received from the server 20. The autonomous vehicle 50 is equipped with an autonomous vehicle control device 500 that includes a communication device 51, a camera 52, a storage device 53, a GPS receiver 54, and a vehicle ECU (Electronic Control Unit) 55. The configurations of the communication device 51, the camera 52, the storage device 53, and the GPS receiver 54 are similar to the configurations of the communication device 41, the camera 42, the storage device 43, and the GPS receiver 44 of the information transmission device 400 of the vehicle 40, and therefore will not be described.

[0027] The vehicle ECU 53 is a computer for controlling the traveling of the autonomous vehicle 50. The vehicle ECU 53 receives a travel command from the server 20 via the network 30, and controls the autonomous vehicle 50 to travel in an appropriate manner based on the received travel command. The vehicle ECU 53 controls various actuators (such as a brake actuator, an accelerator actuator, and a steering actuator) based on the travel command.

[0028] Next, various databases of the storage device 22 will be described with reference to Figures 3 to 5. Although not shown, the storage device 22 may further include a database that associates and manages a vehicle ID with vehicle information of a vehicle to which the vehicle ID is assigned, and the server 20 can identify the vehicle 40 and the autonomously driven vehicle 50 by each vehicle ID.

[0029] The position information database 221 is a database that manages the position information of each of the vehicle 40 and the autonomously driven vehicle 50. As shown in Fig. 3 as an example, the position information database 221 is configured to store the vehicle ID of each of the vehicle 40 and the autonomously driven vehicle 50 and information on the position coordinates of each of the vehicle 40 and the autonomously driven vehicle 50 in real time.

[0030] The attribute information database 222 stores information about pedestrians detected from captured images captured by the vehicle 40. As shown in FIG. 4 as an example, the attribute information database 222 is configured to store, for each pedestrian detected from a captured image, information on the pedestrian ID, gender, age, whether or not the pedestrian is accompanied, and the position coordinates where each pedestrian is detected. In the following description, the gender and age of a pedestrian may be referred to as the attributes of a pedestrian. A method for detecting a pedestrian and a method for estimating the attributes of a pedestrian and whether or not the pedestrian is accompanied will be described later. In the example of FIG. 4, the ages of pedestrians are stored in four categories: children, adults (young), adults (not young), and elderly, but the age categories of pedestrians are not limited to these.

[0031] As shown in FIG. 5 as an example, the risk value database 223 is configured to store a risk value indicating the level of crime risk under each of the conditions set in advance for each combination of gender, age, and whether or not the pedestrian is accompanied. For example, the risk value is set to be higher for children and elderly people than for adults, and higher for women than for men. Also, the risk value is set to be higher for unaccompanied people than accompanied people. For example, the risk value is set to be highest for a female and child who is unaccompanied. The risk value database 223 may be configured to store a risk value set in advance for each element of a pedestrian, such as the gender, age, and whether or not the pedestrian is accompanied, such as 4 points for children, 3 points for women, 2 points for elderly people, and 2 points for unaccompanied people. The method of setting the risk value is not particularly limited, and the danger level according to the attributes of the pedestrian may be set based on data on past crimes.

[0032] Next, the details of each function of the CPU 23 of the server 20 will be described with reference to FIG. 6. In the example of FIG. 6, autonomous vehicles 50a to 50c are patrolling a certain area, a vehicle 40 is traveling on a road, and a pedestrian 60 is present near the vehicle 40. It is assumed that the pedestrian 60 is captured in an image captured by the vehicle 40. In FIG. 6, the vehicle 40 has the configuration of the vehicle 40 shown in FIG. 2, and transmits position information of each of the vehicles 40 and the captured image captured by the vehicle 40 to the server 20 via the network 30. The autonomous vehicles 50a to 50c have the configuration of the autonomous vehicles 50 shown in FIG. 2, and transmit position information of each of the autonomous vehicles 50a to 50c to the server 20 via the network 30.

[0033] The position information management unit 231 of the CPU 23 collects and manages the position information of the vehicle 40 and the autonomously driven vehicles 50a to 50c that have been registered in advance. The position information management unit 231 receives the position information from the vehicle 40 and the autonomously driven vehicles 50a to 50c at a predetermined period, for example, and stores the information in the position information database 221 in association with the vehicle IDs of the vehicle 40 and the autonomously driven vehicles 50a to 50c.

[0034] The pedestrian detection unit 232 receives the captured image captured by the vehicle 40 and transmitted from the vehicle 40, and detects the pedestrian 60 from the captured image. In this embodiment, the pedestrian detection unit 232 detects the pedestrian 60 by analyzing the captured image captured by the vehicle 40. A known method can be used for this image analysis. The pedestrian detection unit 232 detects the position information of the pedestrian 60 based on the position information of the vehicle 40 at the time when the captured image in which the pedestrian 60 is detected was acquired.

[0035] When the pedestrian 60 is detected by the pedestrian detection unit 232, the pedestrian attribute estimation unit 233 estimates the attributes of the pedestrian 60 from the captured image. The pedestrian attribute estimation unit 233 estimates whether the pedestrian 60 is accompanied by anyone, that is, whether there is one pedestrian 60 or multiple pedestrians 60. In this embodiment, the pedestrian attribute estimation unit 233 estimates the attributes of the pedestrian 60 by further analyzing the captured image in which the pedestrian 60 is detected. A known method can be adopted for this image analysis.

[0036] The pedestrian attribute estimation unit 233 detects the face of the detected pedestrian 60, for example, and estimates the gender and age of the pedestrian 60. The pedestrian attribute estimation unit 233 estimates the age of the pedestrian as one of a child, an adult (young), an adult (not young), and an elderly person, for example. In a case where the face of the pedestrian 60 cannot be detected from the captured image, such as when the face of the pedestrian 60 is not clearly captured in the captured image, the pedestrian attribute estimation unit 233 may detect the height, posture, hair color, belongings such as a cane, clothing, etc. of the pedestrian 60, and estimate the gender and age of the pedestrian 60 from these elements. In addition, in a case where the captured image is a video, the walking speed of the pedestrian 60 may be added to the elements for estimating the gender and age.

[0037] The pedestrian attribute estimation unit 233 stores the estimated attributes of the pedestrian 60 and information on the presence or absence of an accompanying person of the pedestrian 60, and the position information of the pedestrian 60 in the attribute information database 222.

[0038] The pedestrian travel area estimation unit 234 estimates the pedestrian travel area A in which the pedestrian 60 is walking based on the captured image. For example, the pedestrian travel area estimation unit 234 estimates the body orientation of the pedestrian 60 from the captured image, and estimates the body orientation of the pedestrian 60 as the travel direction of the pedestrian. Furthermore, when the captured image is a video, the pedestrian travel area estimation unit 234 may estimate the direction in which the pedestrian 60 moves as the travel direction of the pedestrian 60. In this embodiment, the pedestrian travel area estimation unit 234 estimates the travel direction of the pedestrian 60 by further analyzing the captured image in which the pedestrian 60 is detected. A known method can be adopted for this image analysis.

[0039] The pedestrian travel area estimation unit 234 estimates an area of ​​radius X km in the travel direction of the estimated pedestrian 60 based on the point where the pedestrian 60 was detected as the pedestrian travel area A in which the pedestrian 60 is assumed to walk. The pedestrian travel area estimation unit 234 estimates, for example, an area of ​​radius 2 km in the travel direction of the estimated pedestrian 60 based on the point where the pedestrian 60 was detected as the pedestrian travel area A in which the pedestrian 60 is assumed to walk. Note that the method of estimating the pedestrian travel area A is not particularly limited, and it is sufficient if it is possible to estimate an area that includes an area in which the pedestrian 60 is assumed to walk for 10 to 15 minutes, for example.

[0040] The vehicle traffic volume calculation unit 235 calculates the vehicle traffic volume in the pedestrian travel area A. The vehicle traffic volume calculation unit 235 refers to the position information database 221 and calculates the number of vehicles 40 and autonomous vehicles 50 present in the pedestrian travel area A as the vehicle traffic volume. The vehicle traffic volume calculation unit 235 may also calculate the number of vehicles 40 and autonomous vehicles 50 per unit area as the vehicle traffic volume from the number of vehicles 40 and autonomous vehicles 50 present in the pedestrian travel area A. The method of calculating the vehicle traffic volume is not particularly limited. For example, the vehicle traffic volume calculation unit 235 may detect vehicles from captured images transmitted from each of the multiple vehicles 40 and multiple autonomous vehicles 50 managed by the server 20 and store the position information of the detected vehicles to calculate the vehicle traffic volume present in the pedestrian travel area A.

[0041] The risk level calculation unit 236 calculates the risk level of the pedestrian being involved in a crime based on the attributes of the pedestrian 60 and the volume of vehicle traffic within the pedestrian travel area A. The risk level calculation unit 236 refers to the risk value database 223 and acquires a risk value for the pedestrian 60 based on the attributes of the pedestrian 60 and whether or not the pedestrian is accompanied. For example, when the pedestrian's gender is "female", the age is "elderly", and the pedestrian is "unaccompanied", the risk level calculation unit 236 acquires a risk value of "3" for the pedestrian 60 by referring to the risk value database 223 in FIG. 5.

[0042] In addition, the risk level calculation unit 236 may calculate the risk value of the pedestrian 60 by adding up the scores for each element of the pedestrian 60, such as the pedestrian's gender, age, and whether or not the pedestrian is accompanied, as described in Figure 5.

[0043] For example, the risk calculation unit 236 determines whether the amount of vehicle traffic in the pedestrian travel area A is greater than a preset threshold value for vehicle traffic. If the amount of vehicle traffic in the pedestrian travel area A is greater than the threshold value, the risk calculation unit 236 calculates the risk by multiplying the risk value of the pedestrian 60 by 0.5. On the other hand, if the amount of vehicle traffic in the pedestrian travel area A is equal to or less than the threshold value, the risk calculation unit 236 calculates the risk by multiplying the risk value of the pedestrian 60 by the reciprocal of the amount of vehicle traffic per unit area in the pedestrian travel area A. The method of calculating the risk is not particularly limited, and the risk calculation unit 236 may calculate the risk so that the risk is higher when the risk value of the pedestrian 60 is high and the amount of vehicle traffic in the pedestrian travel area A is low. The risk calculation unit 236 may use other elements such as the location where the pedestrian 60 is detected, the time period, and the illuminance in addition to the attributes of the pedestrian, the presence or absence of an accompanying person, and the amount of vehicle traffic, to calculate the risk. Furthermore, data on past crime occurrences may be used to calculate the degree of risk.

[0044] When the risk level is higher than a preset threshold, the patrol command unit 237 transmits a patrol command to the autonomous vehicle 50 to command the autonomous vehicle 50 to patrol the pedestrian proceeding area A. The patrol command unit 237 determines whether the risk level calculated by the risk level calculation unit 236 is higher than a preset threshold level. When the risk level is higher than the threshold level, the patrol command unit 237 refers to the position information database 221 and identifies the autonomous vehicle 50a patrolling the position closest to the pedestrian proceeding area A from among the autonomous vehicles 50a to 50c. The patrol command unit 237 generates a patrol command to command the autonomous vehicle 50a to patrol the pedestrian proceeding area A. The patrol command includes information on a driving route from the current position of the identified autonomous vehicle 50a to the pedestrian proceeding area A and a patrol route for patrolling the pedestrian proceeding area A. The patrol command unit 237 transmits the generated patrol command to the autonomous vehicle 50a to command the autonomous vehicle 50a to patrol the pedestrian proceeding area A.

[0045] [Crime prevention device processing flow] Next, an example of the flow of processing by the CPU 23 according to this embodiment will be described with reference to the flowchart of Fig. 7. In Fig. 7, the vehicle 40 transmits position information of the vehicle 40 and images captured by the vehicle 40 to the server 20 via the network 30. The multiple autonomously driven vehicles 50a-50c transmit position information of each of the autonomously driven vehicles 50a-50c to the server 20 via the network 30. The server 20 collects and manages the position information of the vehicle 40 and the autonomously driven vehicles 50a-50c.

[0046] In step S10 of FIG. 7, the pedestrian detection unit 232 receives the captured image captured by the vehicle 40 and transmitted from the vehicle 40.

[0047] The process proceeds to step S11, where the pedestrian detection unit 232 detects the pedestrian 60 by analyzing the image captured by the vehicle 40.

[0048] If the pedestrian 60 is detected by the pedestrian detection unit 232, the process proceeds to step S12, and the pedestrian detection unit 232 detects the position information of the pedestrian 60 based on the position information of the vehicle 40 at the time the captured image in which the pedestrian 60 is detected was acquired.

[0049] The process proceeds to step S13, where the pedestrian attribute estimation unit 233 estimates the attributes of the pedestrian 60 from the captured image. The pedestrian attribute estimation unit 233 estimates whether the pedestrian 60 is accompanied by anyone. The pedestrian attribute estimation unit 233 stores the estimated attributes of the pedestrian 60, information on the presence or absence of a companion, and position information of the pedestrian 60 in the attribute information database 222.

[0050] The process proceeds to step S14, where the pedestrian travel area estimation unit 234 estimates the body orientation of the pedestrian 60 from the captured image, and estimates the body orientation of the pedestrian 60 as the traveling direction of the pedestrian. If the captured image is a video, the pedestrian travel area estimation unit 234 may estimate the direction in which the pedestrian 60 is moving as the traveling direction of the pedestrian 60.

[0051] The process proceeds to step S15, where the pedestrian travel area estimation unit 234 estimates an area of ​​radius X km in the direction of travel of the estimated pedestrian 60 based on the point where the pedestrian 60 was detected as pedestrian travel area A in which the pedestrian 60 is expected to walk.

[0052] The process proceeds to step S16, where the vehicle traffic volume calculation unit 235 calculates the volume of vehicle traffic in the pedestrian progression area A. For example, the vehicle traffic volume calculation unit 235 refers to the position information database 221 and calculates the number of vehicles 40 and autonomously driven vehicles 50 present in the pedestrian progression area A as the volume of vehicle traffic. In addition, the vehicle traffic volume calculation unit 235 may calculate the number of vehicles 40 and autonomously driven vehicles 50 per unit area as the volume of vehicle traffic from the number of vehicles 40 and autonomously driven vehicles 50 present in the pedestrian progression area A.

[0053] The process proceeds to step S17, where the risk calculation unit 236 calculates the risk of the pedestrian being involved in a crime based on the attributes of the pedestrian 60 and the amount of vehicle traffic in the pedestrian travel area A. The risk calculation unit 236, for example, refers to the risk value database 223 to acquire a risk value for the pedestrian 60 based on the attributes of the pedestrian 60 and whether or not the pedestrian 60 is accompanied. The risk calculation unit 236, for example, determines whether or not the amount of vehicle traffic in the pedestrian travel area A is greater than a preset threshold value for vehicle traffic. If the amount of vehicle traffic in the pedestrian travel area A is greater than the threshold value, the risk calculation unit 236 multiplies the risk value of the pedestrian 60 by 0.5 to calculate the risk. On the other hand, if the amount of vehicle traffic in the pedestrian travel area A is equal to or less than the threshold value, the risk calculation unit 236 multiplies the risk value of the pedestrian 60 by 1.2 to calculate the risk.

[0054] The process proceeds to step S18, where patrol command unit 237 determines whether the risk level calculated in step S17 is higher than a preset threshold value of risk level. If the risk level is higher than the threshold value (YES in step S18), the process proceeds to step S19. On the other hand, if the risk level is equal to or lower than the threshold value (NO in step S18), CPU 23 ends the process of FIG.

[0055] In step S19, the patrol command unit 237 refers to the position information database 221 and identifies the autonomously driven vehicle 50a that is patrolling at a position closest to the pedestrian travel area A from among the autonomously driven vehicles 50a to 50c.

[0056] The process proceeds to step S20, where the patrol command unit 237 generates a patrol command to command the autonomously driven vehicle 50a to patrol the pedestrian progression area A. The patrol command includes information on a driving route from the current position of the autonomously driven vehicle 50a to the pedestrian progression area A and a patrol route for patrolling within the pedestrian progression area A.

[0057] The process proceeds to step S21, where the tour command unit 237 transmits the generated tour command to the autonomously driven vehicle 50, and the CPU 23 ends the process in Fig. 7. The autonomously driven vehicle 50a receives the tour command, generates an operation plan according to the received tour command, and travels according to this operation plan.

[0058] (Variation 1) In the above description, the vehicle traffic volume calculation unit 235 calculates the volume of vehicle traffic in the pedestrian travel area A, and the patrol command unit 237 transmits a patrol command to the autonomous vehicle 50a to patrol the pedestrian travel area A when the risk level is higher than a preset threshold. However, the vehicle traffic volume calculation unit 235 may divide the pedestrian travel area A into a plurality of areas, and further calculate the volume of vehicle traffic in each of the divided pedestrian travel areas A. When the risk level is higher than a preset threshold, the patrol command unit 237 may transmit a patrol command to the autonomous vehicle 50a to patrol each of the divided pedestrian travel areas in accordance with the volume of vehicle traffic in each of the divided pedestrian travel areas A.

[0059] The patrol command unit 237 may adjust the number of vehicles to patrol each of the divided pedestrian travel areas A based on the amount of vehicle traffic in each of the divided pedestrian travel areas A. For example, when the amount of vehicle traffic in one area of ​​the divided pedestrian travel area A is greater than a preset threshold and the amount of vehicle traffic in another area is less than a preset threshold, the patrol command unit 237 may not require patrol in the area with a large amount of vehicle traffic. Then, a patrol command to instruct the two autonomous vehicles 50a and 50b to patrol only the area with a small amount of vehicle traffic may be transmitted to the two autonomous vehicles 50a and 50b. In this way, when the range of the pedestrian travel area A is set to be wide, a patrol command can be generated to increase the number of moving bodies to patrol areas with particularly small amount of vehicle traffic, and crime prevention can be performed more efficiently.

[0060] The patrol command unit 237 may limit the area to be patrolled among the divided pedestrian travel area A based on the amount of vehicle traffic in each of the divided pedestrian travel area A. For example, when the amount of vehicle traffic in one area of ​​the divided pedestrian travel area A is greater than a preset threshold and the amount of vehicle traffic in the other area is less than a preset threshold, the patrol command unit 237 may not require patrol of the area with a large amount of vehicle traffic. Then, a patrol command may be generated to command the autonomous vehicle 50a to patrol only the other two areas. In this way, when the range of the pedestrian travel area A is set to be wide, a patrol command can be generated to patrol only the area with a particularly small amount of vehicle traffic, and crime prevention can be performed more efficiently.

[0061] (Variation 2) In the above description, the crime prevention device according to the present embodiment has been described using an example in which an autonomous vehicle 50 is used as a mobile body that transmits patrol commands. However, depending on the type of crime in which pedestrians are likely to be involved, it may be better to use another type of mobile body. For example, a mobile body with surveillance capabilities, such as an autonomous vehicle 50 equipped with a camera, is suitable for preventing crimes such as calling out to people, dragging them around, and molestation. On the other hand, a mobile body with tracking capabilities, such as a drone, is suitable for preventing crimes such as snatching.

[0062] In addition, the types of crimes that pedestrians are likely to be involved in can be roughly estimated from pedestrian attributes. For example, children are more likely to be approached or dragged around, young women are more likely to be molested, and elderly people and women carrying luggage are more likely to be snatched. In other words, the types of crimes that pedestrians are likely to be involved in can be estimated from pedestrian attributes.

[0063] Therefore, in the case where the crime prevention system 1 has a plurality of types of mobile objects to which patrol commands are sent, the patrol command unit 237 may determine the type of mobile object to which the patrol command is sent based on the type of crime preset in accordance with the attributes of the pedestrian, and send the patrol command to the mobile object. In the case where the captured image is a video, the pedestrian detection unit 232 may detect the occurrence of an incident such as blackmail, assault, or obstruction from the video. In the case where an incident such as blackmail, assault, or obstruction has occurred, the patrol command unit 237 may send a patrol command to a manned vehicle in which a person capable of arresting the criminal is riding, to command the vehicle to head to the location where the incident has been detected. In this way, by sending a patrol command to a mobile object suitable for the type of crime in which the pedestrian is likely to be involved, which is estimated from the attributes of the pedestrian, crime can be prevented more effectively by using the mobile object.

[0064] In addition, depending on the type of crime in which a pedestrian is likely to be involved, it may be better to use multiple mobile units. Therefore, the patrol command unit 237 may determine the number of mobile units to which a patrol command is to be sent based on the type of crime that is preset in accordance with the attributes of pedestrians, and send the patrol command to multiple mobile units. By sending a patrol command to a number of mobile units corresponding to the type of crime in which a pedestrian is likely to be involved, crime can be prevented more effectively by using the mobile units.

[0065] [Effects] As described above, according to this embodiment, the following advantageous effects can be obtained.

[0066] The crime prevention device according to this embodiment includes a pedestrian detection unit, a pedestrian attribute estimation unit, a pedestrian movement area estimation unit, a vehicle traffic volume calculation unit, a risk calculation unit, and a patrol command unit. The pedestrian detection unit receives a captured image transmitted from a vehicle and detects a pedestrian from the captured image. The pedestrian attribute estimation unit estimates attributes of the pedestrian from the captured image. The pedestrian movement area estimation unit estimates a pedestrian movement area in which the pedestrian walks based on the captured image. The vehicle traffic volume calculation unit calculates the vehicle traffic volume within the pedestrian movement area. The risk calculation unit calculates the risk of the pedestrian being involved in a crime based on the pedestrian attributes and the vehicle traffic volume within the pedestrian movement area. When the risk level is higher than a preset threshold, the patrol command unit transmits a patrol command to the mobile body to command the mobile body to patrol the pedestrian movement area.

[0067] This makes it possible to calculate the risk of a pedestrian becoming involved in a crime based on the pedestrian's attributes and the volume of vehicle traffic in the pedestrian's travel area. The risk can be increased when a pedestrian has attributes that make them more likely to become involved in a crime and is walking in an area with low vehicle traffic. If the risk is higher than a threshold, a patrol command can be sent to the mobile body to instruct it to patrol the pedestrian's travel area. This makes it possible to more effectively prevent crime by using the mobile body.

[0068] In addition, the vehicle traffic volume calculation unit of the crime prevention device according to this embodiment divides the pedestrian movement area into a plurality of areas and further calculates the vehicle traffic volume in each of the divided pedestrian movement areas. When the risk level is higher than a preset threshold, the patrol command unit transmits a patrol command to the mobile unit to patrol each of the divided pedestrian movement areas according to the vehicle traffic volume in each of the divided pedestrian movement areas.

[0069] This makes it possible to generate a patrol command to adjust the number of moving bodies to patrol based on the amount of vehicle traffic in each of the divided pedestrian travel areas. When the range of the pedestrian travel area is set wide, a patrol command can be generated to increase the number of moving bodies to patrol in areas with particularly low vehicle traffic. Also, based on the amount of vehicle traffic in each of the divided pedestrian travel areas, a patrol command can be generated to limit the area in which the moving bodies will patrol. When the range of the pedestrian travel area is set wide, a patrol command can be generated to patrol only areas with particularly low vehicle traffic. Crime can be prevented more efficiently by using the moving bodies.

[0070] Furthermore, the patrol command section of the crime prevention device according to this embodiment determines the type of mobile body to which a patrol command is to be sent based on the type of crime that is preset in accordance with the attributes of pedestrians.

[0071] This makes it possible to transmit a patrol command to a type of mobile object that corresponds to the type of crime in which a pedestrian is likely to be involved, and to command a mobile object suitable for preventing crimes in which a pedestrian is likely to be involved to patrol an area in which pedestrians are traveling. This makes it possible to more effectively prevent crimes by utilizing mobile objects.

[0072] Furthermore, the patrol command section of the crime prevention device according to this embodiment determines the number of mobile bodies to which a patrol command is to be sent based on the type of crime that is preset in accordance with the attributes of pedestrians.

[0073] This makes it possible to transmit patrol commands to a number of mobile bodies corresponding to the types of crimes in which pedestrians are likely to be involved, and to command a number of mobile bodies appropriate for preventing crimes in which pedestrians are likely to be involved to patrol areas in which pedestrians are moving. This makes it possible to use mobile bodies for more effective crime prevention.

[0074] As described above, the embodiment of the present invention has been described, but the description and drawings forming a part of this disclosure should not be understood as limiting this invention. From this disclosure, various alternative embodiments, examples and operating techniques will become apparent to those skilled in the art. [Explanation of symbols]

[0075] 23 Crime Prevention Device (CPU) 232 Pedestrian detection unit 233 Pedestrian Attribute Estimation Unit 234 Pedestrian movement area estimation unit 235 Vehicle Traffic Calculation Department 236 Risk Calculation Unit 237 Patrol Command Department 40 vehicles 50 Mobile (Autonomous Vehicles) 60 Pedestrians A Pedestrian movement area

Claims

1. a pedestrian detection unit that receives a captured image transmitted from a vehicle and detects a pedestrian from the captured image; a pedestrian attribute estimation unit that estimates attributes of the pedestrian from the captured image; a pedestrian travel area estimation unit that estimates a pedestrian travel area in which the pedestrian is walking based on the captured image; a vehicle traffic volume calculation unit that calculates a vehicle traffic volume within the pedestrian travel area; a risk calculation unit that calculates a risk of the pedestrian being involved in a crime based on attributes of the pedestrian and the amount of vehicle traffic in the pedestrian travel area; a patrol command unit that transmits a patrol command to the mobile object to command the mobile object to patrol the pedestrian travel area when the risk level is higher than a preset threshold value; A crime prevention device comprising:

2. The vehicle traffic volume calculation unit divides the pedestrian travel area into a plurality of areas, and further calculates a vehicle traffic volume within each of the divided pedestrian travel areas; When the risk level is higher than the preset threshold value, the patrol command unit transmits a patrol command to the moving body to patrol each of the divided pedestrian travel areas in accordance with a vehicle traffic volume in each of the divided pedestrian travel areas.

2. The crime prevention device according to claim 1.

3. The patrol command unit determines the type of the moving object to which the patrol command is to be sent based on a type of crime preset in accordance with an attribute of the pedestrian.

3. A crime prevention device according to claim 1 or 2.

4. The patrol command unit determines the number of the mobile units to which the patrol command is to be sent based on a type of crime that is preset in correspondence with an attribute of the pedestrian.

3. A crime prevention device according to claim 1 or 2.

5. Receives captured images transmitted from a vehicle; Detecting a pedestrian from the captured image; Estimating attributes of the pedestrian from the captured image; estimating a pedestrian travel area in which the pedestrian is walking based on the captured image; Calculating the amount of vehicle traffic within the pedestrian travel area; calculating a risk of the pedestrian being involved in a crime based on attributes of the pedestrian and the amount of vehicle traffic in the pedestrian travel area; When the risk level is higher than a preset threshold, a patrol command is transmitted to the mobile body to instruct the mobile body to patrol the pedestrian travel area. Crime prevention methods.

Images