Information processing system, information processing method, and program

Abstract

An information processing system, an information processing method, and a program are provided that analyze video captured by a camera and output data. [Solution] In an information processing system 1000 in which a server device 100, client devices 110, 120, and multiple surveillance cameras 160 are communicatively connected via a network 150, a control unit of the server device sets camera attributes for each surveillance camera and registers person attributes for each person, and when specific detection conditions are met in the video, stores detection information related to the detection, including the attributes of the camera related to the video and the attributes of the person related to the video, and outputs the detection information to the client device.

Description

[Technical field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] In recent years, with the spread of cloud cameras, efforts to promote digital transformation at work sites by analyzing video data (hereafter simply referred to as video) have been increasing in various industries. The first important aspect of digital transformation is to visualize the flow of people at the work site. One of the key points in this regard is to accurately count the number of people. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2005-234947 A Summary of the Invention [Problem to be solved by the invention]

[0004] In a company, multiple cameras are installed in each store. When counting the number of people, for example, flexible measures are required, such as not counting employees in the same store as the number of people, but counting employees in different stores as customers. The technology in Patent Document 1 does not take such situations into consideration, making it difficult to use. [Means for solving the problem]

[0005] According to one aspect of the present disclosure, an information processing system is provided that analyzes video captured by a camera and outputs data. The information processing system sets camera attributes for each camera, registers person attributes for each person, and when a predetermined detection condition is satisfied in the video, stores the attributes of the camera related to the video and the attributes of the person related to the video as detection information related to the detection, and outputs the detection information. According to one aspect of the present disclosure, there is provided an information processing system that analyzes video captured by a camera and outputs data. The information processing system sets camera attributes for each camera, registers person attributes for each person, and when a predetermined detection condition is satisfied in the video, stores detection information related to the detection including information indicating whether the camera attributes related to the video correspond to the person attributes related to the video, and outputs the detection information. According to one aspect of the present disclosure, there is provided an information processing system that analyzes video captured by a camera and outputs data. The information processing system sets camera attributes for each camera, registers person attributes for each person, and when a predetermined detection condition is satisfied in the video, counts the number of people, except when the person in the video is a registered person and the person attributes correspond to the attributes of the camera in the video, and outputs the count result. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 is a diagram illustrating an example of a system configuration of an information processing system. [Diagram 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of the server device. [Diagram 3] FIG. 3 is a diagram illustrating an example of a hardware configuration of the client device. [Figure 4] FIG. 4 is a diagram illustrating an example of a hardware configuration of a surveillance camera. [Diagram 5] FIG. 5 is a diagram illustrating an example of the monitoring camera table. [Figure 6] FIG. 6 is a diagram showing an example of the staff registration screen. [Figure 7] FIG. 7 is a diagram showing an example of a modal window. [Figure 8] FIG. 8 is a diagram illustrating an example of the registered staff table. [Figure 9] FIG. 9 is a flowchart illustrating an example of information processing for setting detection conditions and registering excluded persons, which is executed by the server device. [Figure 10]FIG. 10 is a diagram showing an example of a list screen of surveillance cameras. [Figure 11] FIG. 11 is a diagram showing an example of the detection condition setting screen. [Figure 12] FIG. 12 is a flowchart showing an example of a process for counting the number of people. [Figure 13] FIG. 13 is a diagram illustrating an example of the detection result table. [Figure 14] FIG. 14 is a diagram showing an example of the count result list screen. [Figure 15] FIG. 15 is a diagram showing an example of a measurement data (CSV) creation screen. [Figure 16] FIG. 16 is a diagram showing an example of the detection group setting screen. [Figure 17] FIG. 17 is a diagram showing an example of a screen for selecting the surveillance cameras to be grouped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Various characteristic features shown in the following embodiment (including modified examples, which also apply hereinafter) can be combined with each other.

[0008] <Embodiment 1> 1. System configuration diagram Fig. 1 is a diagram showing an example of a system configuration of an information processing system 1000. As shown in Fig. 1, the information processing system 1000 includes, as a system configuration, a server device 100, a client device 110, a client device 120, and a plurality of surveillance cameras 160. The server device 100, the client device 110, the client device 120, and the surveillance cameras 160 are communicatively connected via a network 150. The network 150 may include either or both of a WAN (Wide Area Network) and the Internet. The network 150 is configured to enable communication between a plurality of devices connected to the network 150 via wired and wireless communication.

[0009] The information processing system 1000 is a system that analyzes images captured by a plurality of surveillance cameras 160 and outputs data.

[0010] The server device 100 is a device that provides the main functions of the information processing system 1000, and executes the main processes of the embodiment described below. The server device 100 is a device that analyzes images captured by multiple surveillance cameras 160, and outputs (displays) the data to the client device 110 or the client device 120.

[0011] The surveillance camera 160 is a camera installed for the purpose of monitoring and / or recording. In the specification, the surveillance camera 160 is installed at each of a plurality of locations in a given store. Examples of the plurality of locations include the store entrance (east entrance), the store entrance (north entrance), the employee entrance, the back yard, and in front of the cash register. Note that these are merely examples and do not limit the locations where the surveillance cameras 160 are installed. For simplification, only three surveillance cameras 160 are shown in FIG. 1, but two, four or more may be used. It is sufficient that the information processing system 1000 includes a plurality of surveillance cameras 160. In addition, in the specification, the surveillance camera 160 is mainly installed at one store, but a plurality of cameras may be installed at each of a plurality of stores.

[0012] The client device 110 is a terminal device operated by the owner or manager of the surveillance camera 160 .

[0013] Client device 120 is a terminal device operated by a manager or the like of a store in which surveillance camera 160 is installed. Client device 120 displays screens such as those shown in Figs. 6 to 7, 10 to 11, and 14 to 17, which will be described later. In the following description, the client device 110 and the client device 120 are simply referred to as the client device.

[0014] Here, the information processing system described in the claims may be composed of multiple devices or may be composed of one device. When the information processing system described in the claims is composed of one device, an example of the device is the server device 100. When the information processing system described in the claims is composed of multiple devices, an example of the multiple devices is the server device 100, at least one or more surveillance cameras 160 among the multiple surveillance cameras 160, or the server device 100, at least one or more surveillance cameras 160 among the multiple surveillance cameras 160 and the client device 110 or the client device 120, or a cloud server composed of multiple server devices that provide the functions of the server device 100.

[0015] 2. Hardware Configuration (1) Hardware Configuration of Server Device 100 FIG. 2 is a diagram illustrating an example of a hardware configuration of the server device 100. As shown in FIG. 2, the server device 100 includes, as its hardware configuration, a control unit 210, a storage unit 220, a communication unit 230, and an internal bus 240. The control unit 210, the storage unit 220, and the communication unit 230 are electrically connected via the internal bus 240.

[0016] The control unit 210 is a CPU (Central Processing Unit) or the like, and controls the entire server device 100 .

[0017] The storage unit 220 is any one of a hard disk drive (HDD), a read only memory (ROM), a random access memory (RAM), a solid state drive (SSD), etc., or any combination of these, and stores a program, data used when the control unit 210 executes a process based on the program, etc. The storage unit 220 is an example of a storage medium. Examples of data used when the control unit 210 executes a process based on the program include video data sent from the surveillance camera 160, data shown in Figs. 5, 8, 13, etc., which will be described later, data set via a screen, etc., which will be described later, data displayed on a screen, etc., which will be described later, etc.

[0018] In the specification, data used by the control unit 210 when executing processing based on a program is described as being stored in the storage unit 220, but the data may be stored in a storage unit of another device that can communicate with the server device 100. The data may be stored in a storage unit of any device as long as the control unit 210 can refer to or obtain the data. The control unit 210 executes processing based on a program stored in the storage unit 220, thereby realizing the functions of the server device 100 and the processing of the flowcharts shown in Figs. 9 and 12 described below. Note that, although this processing is described as being mainly executed by the server device 100, it may instead be executed by any of the client device 110, client device 120, or multiple surveillance cameras 160.

[0019] The communication unit 230 connects the server device 100 to the network 150 and controls communications with other devices.

[0020] The number of hardware components of each of the control unit 210, the storage unit 220, and the communication unit 230 is not limited to one. For example, a plurality of control units may be included in the server device 100. The same applies to the client device 110 and the client device 120 described below.

[0021] (2) Hardware configuration of the client device FIG. 3 is a diagram showing an example of a hardware configuration of the client device 110. As shown in FIG. 3, client device 110 includes, as its hardware configuration, a control unit 310, a storage unit 320, an input unit 330, an output unit 340, a communication unit 350, and an internal bus 360. Control unit 310, storage unit 320, input unit 330, output unit 340, and communication unit 350 are electrically connected via internal bus 360.

[0022] The control unit 310 is a CPU or the like, and controls the entire client device 110 .

[0023] The storage unit 320 is any one of an HDD, a ROM, a RAM, an SSD, etc., or any combination of these, and stores programs, data used when the control unit 310 executes processing based on the programs, etc. The storage unit 320 is an example of a storage medium.

[0024] In the specification, the data used by control unit 310 when executing processing based on a program is described as being stored in storage unit 320, but the data may be stored in a storage unit of another device capable of communicating with client device 110. The data may be stored in a storage unit of any device as long as control unit 310 can refer to or obtain the data. The functions and the like of client device 110 are realized by control unit 310 executing processing based on the program stored in storage unit 320.

[0025] The input unit 330 is a device that inputs information to the client device 110 in response to an operation by an operator. The input unit 330 accepts an operation input made by a user. The operation input is transferred as a command signal to the control unit 310 via the internal bus 360. The control unit 310 may execute a predetermined control and / or calculation based on the transferred command signal as necessary. The input unit 330 may be included in the housing of the client device 110 or may be externally attached. For example, the input unit 330 may be implemented as a touch panel integrated with the output unit 340. When the input unit 330 is implemented as a touch panel, the user can input a tap operation, a swipe operation, or the like to the input unit 330. As the input unit 330, a switch button, a mouse, a track pad, a keyboard, or the like can be adopted instead of a touch panel.

[0026] The output unit 340 is, for example, a display unit typified by a display, and is a device that outputs (displays) information as a screen of a graphical user interface (GUI) that can be operated by a user. The output unit 340 may be included in the housing of the client device 110, or may be attached externally. More specifically, the output unit 340 may be implemented as a display device such as a liquid crystal display, an organic EL (Electron-Luminescence) display, or a plasma display. It is preferable that these display devices are implemented by selectively using them according to the type of the client device 110.

[0027] The communication unit 350 connects the client device 110 to the network 150 and manages communications with other devices.

[0028] The hardware configuration of the client device 120 is similar to that of the client device 110 .

[0029] In the present specification, a PC (Personal Computer) is used as an example of client device 110 and client device 120. However, client device 110 and client device 120 may be a smartphone, a tablet computer, or the like. The client device may be any device that can display a screen as described below, accept user operations via the screen, or the like, and transmit information to server device 100.

[0030] (3) Hardware configuration of the surveillance camera 160 FIG. 4 is a diagram showing an example of the hardware configuration of the surveillance camera 160. As shown in FIG. 4, the hardware configuration of the surveillance camera 160 includes a control unit 410, a storage unit 420, an image capturing unit 430, a communication unit 440, and an internal bus 450. The control unit 410, the storage unit 420, the image capturing unit 430, and the communication unit 440 are electrically connected via the internal bus 450.

[0031] The control unit 410 is a CPU or the like, and controls the entire surveillance camera 160 .

[0032] The storage unit 420 is any one of an HDD, a ROM, a RAM, an SSD, etc., or any combination of these, and stores programs, data used when the control unit 410 executes processing based on the programs, etc. The storage unit 420 is an example of a storage medium.

[0033] In the specification, the data used by control unit 410 when executing processing based on a program is described as being stored in storage unit 420, but the data may be stored in a storage unit of another device capable of communicating with surveillance camera 160. The data may be stored in a storage unit of any device as long as control unit 310 can refer to or obtain the data. The functions of surveillance camera 160 are realized by control unit 410 executing processing based on the program stored in storage unit 420.

[0034] The photographing unit 430 is a camera that photographs a subject. The camera includes, for example, an image sensor, a lens, and an IR cut filter.

[0035] The communication unit 440 connects the surveillance camera 160 to the network 150 and controls communication with other devices.

[0036] 3. Information Processing The information processing of the first embodiment will be described below.

[0037] (1) Overview of the process (1-1) The control unit 210 sets the attributes of the surveillance camera 160 for each surveillance camera 160 . The control unit 210 registers the attributes of each person. When a predetermined detection condition is satisfied in the video, control unit 210 stores the detection information related to the detection, including the attributes of surveillance camera 160 related to the video and the attributes of the person related to the video. The control unit 210 outputs the detection information. By performing such processing, accurate detection information can be output flexibly.

[0038] (1-2) The control unit 210 sets the attributes of the surveillance camera 160 for each surveillance camera 160 . The control unit 210 registers the attributes of each person. When a predetermined detection condition is satisfied in the video, the control unit 210 stores detection information related to the detection, including information indicating whether the attributes of the surveillance camera 160 related to the video correspond to the attributes of the person related to the video. The control unit 210 outputs the detection information. By performing such processing, accurate detection information can be output flexibly.

[0039] (1-3) The control unit 210 sets the attributes of the surveillance camera 160 for each surveillance camera 160 . The control unit 210 registers the attributes of each person. When a predetermined detection condition is satisfied in the video, the control unit 210 counts the number of people, except when the person in the video is a registered person and the attributes of the person correspond to the attributes of the surveillance camera 160 in the video. The control unit 210 outputs the result of the counting. By performing such processing, it is possible to flexibly output accurate count results.

[0040] (1-4) The control unit 210 sets a department for the surveillance camera 160. The department of the surveillance camera 160 is an example of an attribute of the surveillance camera 160. As a more specific example of the department, the description mainly uses a store as an example. However, the department is not limited to a store, and may be, for example, a floor of a store (first floor, second floor, basement, etc.) or an area related to the store (the name of a region including multiple stores in relation to the location where the stores are installed). Examples of areas related to the store include, for example, Shinagawa Ward, Shibuya Ward, Meguro Ward, etc., Tokyo, Kanagawa Prefecture, Chiba Prefecture, etc., Kanto, Tohoku, Hokuriku, etc. Note that the information may be a combination of any two or more of the store, the floor of the store, the area related to the store, etc. The control unit 210 sets information relating to the conditions for counting the number of people in the video captured by the surveillance camera 160. The control unit 210 registers people to be excluded when counting the number of people in association with the person's department. The person's department is an example of a person's attribute. As with the departments of the surveillance camera 160, the person's department will be mainly explained using a store as an example in the specification. However, as with the departments of the surveillance camera 160, the person's department is not limited to a store, and may be, for example, a floor of a store or an area related to a store.

[0041] When a condition is satisfied in a video and a person included in the video is not a registered person, the control unit 210 counts the person as the number of people. When a condition is satisfied in the video, if the person included in the video is a registered person and the department of the person associated with the registered person is not the same as the department of the surveillance camera 160 related to the video, the control unit 210 counts the person as a number of people. When a condition is satisfied in the video, if the person included in the video is a registered person and the department of the person associated with the registered person is the same as the department of the surveillance camera 160 related to the video, the control unit 210 does not count the person as a number of people. The control unit 210 outputs the result of the count.

[0042] By carrying out such processing, it is possible to perform a flexible and accurate headcount.

[0043] (2) Details of the processing (2-0)Pretreatment First, as settings in advance, the angle to be determined as the front angle of the face, the angle difference to be determined as being sufficiently different from the front angle, the reliable number of image pixels, the designated time for obtaining the best shot, etc. These settings are set, for example, according to the user's instruction via the client device 110, and are stored in the storage unit of each surveillance camera 160 (or the server device 100), etc.

[0044] In the information processing system 1000, basically, each surveillance camera 160 always captures video and transmits it to the server device 100 for storage, and at the same time, a process of extracting the best shot is also performed. Here, the best shot is an optimal face image (or whole body image) of the same person captured from different predetermined angles. This process is mainly performed on the surveillance camera 160 side, but may be performed on the server device 100 side. The advantage of performing the process on the surveillance camera 160 side is that the processing load and usage fee on the server device 100 side are reduced accordingly, but on the other hand, the performance of the control unit 410 and / or the storage unit 420, etc. for performing the corresponding process on the surveillance camera 160 side is required.

[0045] The details of the best shot extraction process are as follows. First, the control unit 410 of the surveillance camera 160 tracks a full-body image of a person from the camera's video. Next, the control unit 410 detects a face from the data of the full-body image and tracks it. Then, the control unit 410 detects the angle from the tracked face, and sets the image that falls within the range of the front angle of the face as the first best shot, and records the angle of the face in the storage unit 420 or the like. The first best shot may be the one with the highest score using an AI model that detects, for example, a front image of the face from the video.

[0046] Furthermore, the control unit 410 continues to detect the angle of the tracked face, and if there is a face whose angle is equal to or greater than the angle difference from the front angle of the face angle of the best shot 1, it stores it in the storage unit 420 or the like as the best shot 2. The best shot 2 may be the one with the highest score using an AI model that detects, for example, an image of a face facing left (or right) from the camera from the video.

[0047] Furthermore, the control unit 410 saves an image with an angle difference from both the first and second best shots as the third best shot. For the third best shot, for example, an AI model that detects an image of a face facing right (or left) relative to the camera may be used to adopt the image with the highest score. Furthermore, the control unit 410 may save a whole-body image (or a bust-up image, etc.) of a person corresponding to at least one of the first, second, and third best shots as the fourth best shot. A bust-up image is an image showing the upper half of a person's body. For the fourth best shot, an AI model that detects a whole-body image (or a bust-up image, etc.) from a video may be used to adopt the image with the highest score.

[0048] The control unit 410 may continue to detect the tracked face, and if an image that is within the front angle range and has a number of pixels exceeding the reliable image number, replace the 1st best shot. Similarly, the control unit 410 may replace the 2nd, 3rd, and 4th best shots. The control unit 410 may also stop extracting the best shot if the designated time for obtaining the best shot has elapsed. The control unit 410 adopts the 1st, 2nd, 3rd, and 4th best shots that have the highest scores from each viewpoint, but may also adopt multiple best shots including those with the next highest scores from each viewpoint. Then, when tracking ends, the control unit 410 transmits the 1st, 2nd, 3rd, and 4th best shots to the server device 100 together with metadata indicating the camera and date and time that each image was taken.

[0049] The control unit 210 of the server device 100 receives the 1st, 2nd, 3rd, and 4th best shots and metadata indicating the camera and date / time when each image was taken, and stores the images in association with each other / each image and metadata. At this stage, the control unit 210 may perform averaging of face images based on the 1st, 2nd, and 3rd best shots, calculate facial features, and store the results in association with the images and metadata. Hereinafter, a set of such associated information (1st, 2nd, 3rd, and 4th best shots / metadata indicating the camera and date / time when each image was taken / facial features) is referred to as best shot information. The best shot information is stored in a predetermined storage area such as the storage unit 220 as a plurality of mutually different best shot information for each person / scene.

[0050] (2-1) Setting the category of the surveillance camera 160 The control unit 210 sets the department of the surveillance camera 160. For example, the control unit 210 sets data in the surveillance camera table 500 including the department of the surveillance camera 160 in response to an instruction from a store manager or the like via the client device 120. The surveillance camera table 500 is stored in a predetermined storage area such as the storage unit 220 of the server device 100.

[0051] Fig. 5 is a diagram showing an example of a surveillance camera table 500. As shown in Fig. 5, the surveillance camera table 500 includes a camera ID 510, a camera name 520, and a camera department 530 as data items. In the camera ID 510, identification information (camera ID) for identifying the surveillance camera 160 is stored. In the camera name 520, the name of the surveillance camera 160 (camera name) is stored. A camera category (camera category) is set in the camera category 530. This category is, for example, as described above, information indicating a store, a store floor, an area related to a store, or the like, or information combining any two or more of them.

[0052] (2-2) Staff registration In addition, the control unit 210 registers staff members in the information processing system 1000. Staff members can also be considered people. For example, the control unit 210 generates and transmits a staff member registration screen 600 as shown in FIG. 6 in response to a request from a store manager or the like via the client device 120. 6 is a diagram showing an example of a staff registration screen 600. The staff registration screen 600 includes a navigation menu and an area 620. When a staff registration button 610 in the navigation menu is selected, information about the registered staff (a list of registered staff, facial photographs of each staff member, various basic information, etc.) and a staff registration button 630 are displayed in the area 620.

[0053] When it is detected that the staff registration button 630 has been selected, the control unit 210 generates a modal window 700 as shown in FIG. FIG. 7 is a diagram showing an example of a modal window 700. The modal window 700 is an example of a screen for registering a new staff member. A store manager or the like can add a photo (image) of the store staff member via the modal window 700. More preferably, the store manager or the like registers the photo (image) of the store staff member in the information processing system 1000 by selecting a photo of the store staff member from photos stored in a predetermined storage area such as the storage unit 220 via the modal window 700. In addition, a store manager or the like can enter the staff member's name in an input area 710 and the department to which the staff member belongs in an input area 720 via a modal window 700, and can register the staff member. The department to which the staff member belongs is the same concept as the camera department described above.

[0054] In addition, the store manager or the like can add photos of staff members by selecting the "Add Photo" button 730 included in the modal window 700. In this case, the store manager or the like should select photos of the same staff member taken from various angles. Furthermore, for example, when a staff member's photo is selected as a cover photo in the modal window 700 and the "Add Photo" button 730 is selected, the control unit 210 may extract best shot information related to the image selected as the cover photo from the storage unit 220, etc., and display the best shot image included in the best shot information as a suggested image for the staff member's face photo. The control unit 210 may then register the best shot image selected from the suggestion screen as a further face image of the staff member. The cover photo image selected in the modal window 700, or the cover photo image selected in the modal window 700 and the best shot image selected from the suggestion screen, will hereinafter also be referred to simply as a staff image.

[0055] As another example, when a photo of a staff member is selected as a cover photo in the modal window 700, the control unit 210 may extract the selected photo (image) and best shot information related to the selected image from the memory unit 220, etc., and register the best shot image included in the best shot information as a staff image in the memory unit 220, etc.

[0056] Fig. 8 is a diagram showing an example of a registered staff table 800. As shown in Fig. 8, the registered staff table 800 includes a person ID 810, a person name 820, a person department 830, and a person image 840 as data items. Identification information for identifying a person is stored in the person ID 810. The identification information for identifying a person is assigned by the control unit 210 every time a new staff member is registered via the modal window 700. The name of the staff member is stored in the person name 820. The name input in the input area 710 is stored. The staff department is stored in the person department 830. The department input in the input area 720 is stored. This department is, for example, as described above, information indicating the store, the store floor, the area related to the store, or information combining any two or more of them. A staff member's photo is stored in the person image 840. When multiple photos are registered in the modal window 700, multiple photos of the person are stored in the person image 840. Alternatively, when a photo is selected in the modal window 700, the person image 840 stores the selected photo together with a staff member image related to this photo.

[0057] (2-2) Setting detection conditions and registering excluded persons FIG. 9 is a flowchart showing an example of information processing executed by the server device 100 for setting detection conditions and registering excluded persons. In step S910, the control unit 210 determines whether or not a display request for a list screen has been received from the client device 120, etc. When a predetermined operation is performed by a manager of the store in which the surveillance camera 160 is installed, the client device 120 transmits a display request for a list screen as shown in Fig. 10, which will be described later, to the server device 100. If control unit 210 determines that a request to display the list screen has been received, the process proceeds to step S920. If control unit 210 determines that a request to display the list screen has not been received, the process repeats the process of step S910.

[0058] In step S920, the control unit 210 generates a list screen and transmits it to the requesting client device 120. Upon receiving the list screen, the client device 120 displays the list screen on the output unit 340.

[0059] 10 is a diagram showing an example of a list screen 1010 of surveillance cameras 160. On the list screen 1010, a list of surveillance cameras 160 managed by a corresponding administrator or the like is displayed as devices. When the administrator or the like selects one of the surveillance cameras 160 in the list (for example, the store entrance (east entrance) 1020 in FIG. 7), device selection information including identification information for identifying the selected surveillance camera 160 is transmitted from the client device 120 to the server device 100.

[0060] In step S930, control unit 210 determines whether or not a device (surveillance camera 160) has been selected in client apparatus 120. When control unit 210 receives device selection information from client apparatus 120, it determines that a device has been selected. If control unit 210 determines that a device has been selected, it advances the process to step S940, and if control unit 210 determines that a device has not been selected, it repeats the process of step S920 (or step S930).

[0061] In step S940, the control unit 210 generates a detection condition setting screen and transmits it to the requesting client device 120. Upon receiving the detection condition setting screen, the client device 120 displays the detection condition setting screen on the output unit 340.

[0062] FIG. 11 is a diagram showing an example of a detection condition setting screen 1100. The detection condition setting screen 1100 is a screen for setting detection conditions. The detection condition setting screen 1100 is configured to be able to set an area for detecting people (hereinafter also referred to as a detection area) while playing back a video. That is, information related to the conditions for counting the number of people in the video includes information on the area of ​​the video. Furthermore, the detection condition setting screen 1100 is configured to be able to set a line (hereinafter also referred to as a passing line) for detecting the passage of people IN and / or OUT while playing back a video. That is, information related to the conditions for counting the number of people in the video includes information on the line of the video.

[0063] Areas 1115, 1125, 1135, and 1145 are detection areas set on the detection condition setting screen 1100. Different areas are displayed in different manners on the detection condition setting screen 1100. Examples of different manners include different colors, different arrangements of explanatory text, and the like. The back of the store button 1110 on the navigation menu corresponds to area 1125 . A bench seat button 1120 in the navigation menu corresponds to area 1115 . The wall seat for two button 1130 in the navigation menu corresponds to area 1135 . The center in-store button 1140 of the navigation menu corresponds to area 1145 . A line 1155 and a line 1165 are passing lines set on the detection condition setting screen 1100. Different lines are displayed in different manners on the detection condition setting screen 1100. Examples of different manners include different colors, different arrangements of explanatory text, and the like. The TOILET FRONT_UP button 1150 on the navigation menu corresponds to line 1155 . The large table side button 1160 in the navigation menu corresponds to the line 1165 .

[0064] In step S950, the control unit 210 determines whether or not detection conditions have been set on the detection condition setting screen 1100. If a detection area and / or a passing line, etc. have been set on the detection condition setting screen 1100 using an image, the control unit 210 determines that the detection conditions have been set. If a detection area and / or a passing line, etc. have not been set on the detection condition setting screen 1100 using an image, the control unit 210 determines that the detection conditions have not been set. If the control unit 210 determines that the detection conditions have been set, it advances the process to step S960. If the control unit 210 determines that the detection conditions have not been set, it returns the process to step S940.

[0065] In step S960, the control unit 210 sets, as detection conditions, information such as the position and size on the image of one or more detection areas (in the example of FIG. 11, areas 1115, 1125, 1135, and 1145) and / or one or more passing lines (in the example of FIG. 11, lines 1155 and 1165) set via the detection condition setting screen 1100. The control unit 210 stores the detection conditions in a predetermined storage area such as the storage unit 220 in association with a camera ID that identifies the camera that captured the image displayed on the detection condition setting screen 1100.

[0066] In step S970, the control unit 210 determines whether or not an excluded person has been set on the detection condition setting screen 1100. For example, the manager of the surveillance camera 160 or the like right-clicks on the detection condition setting screen 1100 to display a list of registered staff from a context menu or the like that is displayed, and sets the person to be excluded from the count by selecting the person (staff member) to be excluded from the count from the list. A person to be excluded from the count is hereinafter also referred to as an excluded person. If an excluded person is set via the detection condition setting screen 1100, the control unit 210 advances the process to step S980. If an excluded person is not set via the detection condition setting screen 1100, the control unit 210 ends the process shown in FIG.

[0067] In step S980, the control unit 210 sets data of an excluded person to be excluded from the count set via a predetermined screen such as the detection condition setting screen 1100 as excluded person data in the storage unit 220 or the like. The excluded person data includes, in association with each other, a detection condition (hereinafter also simply referred to as a detection condition) associated with a camera ID set on the detection condition setting screen 1100 and information of an excluded person to be excluded from detection of a person to be counted by the detection condition set on the detection condition setting screen 1100. Note that the information of the excluded person may be detection conditions related to all detection areas and passing lines related to a predetermined video set on the detection condition setting screen as shown in FIG. 11, or may be detection conditions related to each of the detection areas and passing lines set on the detection condition setting screen as shown in FIG. 11. That is, the control unit 210 may accept settings of excluded persons to be excluded for all detection conditions set on the screen as shown in FIG. 11, or may accept settings of excluded persons to be excluded for each detection condition set on the screen as shown in FIG. 11. However, for the sake of simplicity, the following description will be given assuming that the detection conditions relate to all detection areas and passing lines related to a specific image set on the detection condition setting screen as shown in FIG. 11 (i.e., related to the camera ID that identifies the surveillance camera 160 that captured the image displayed on the detection condition setting screen).

[0068] The excluded person information includes the registered staff record of the selected staff member among the staff members registered in the registered staff table 800. The registered staff record is data stored in the person ID 810, person name 820, person department 830, and person image 840 of the corresponding staff member.

[0069] (2-3) Counting the number of people FIG. 12 is a flowchart showing an example of a process for counting the number of people. In step S1200, control unit 210 determines whether or not a person has been detected from the images sent from each of multiple surveillance cameras 160. For example, the control unit 210 inputs a video to the trained model. This trained model (hereinafter also referred to as a human detection trained model) is a human detection trained model trained with a video as input data, and with whether or not a person is included in the video, an image of the person (a face image of the person) if the person is included in the video, a detected location (a minimum rectangular area including the detected person), and movement data of the person obtained from the video before and after in time series as output data. As another example, the control unit 210 may perform image analysis on the video to output as analysis results whether or not a person is included in the video, an image of the person (a face image of the person) if the person is included in the video, a detected location, and movement data of the person obtained from the video before and after in time series. However, in the following, for the sake of simplicity of explanation, the control unit 210 will be described as using the human detection trained model to acquire whether or not a person is included in the video, a detected location if the person is included in the video, and movement data of the person obtained from the video before and after in time series. The control unit 210 obtains output data from the human detection trained model. If control unit 210 determines that a person has been detected, the process proceeds to step S1210, and if control unit 210 determines that a person has not been detected, the process of step S1200 is repeated.

[0070] In step S1210, the control unit 210 determines whether the detected person is a person who satisfies the detection condition. As described above, the detection condition is associated with a camera ID. The control unit 210 acquires the detection condition associated with the camera ID of the camera that captured the video in which the person was detected, and determines whether the detected person is a person who satisfies the detection condition based on the detection condition and the detected location and the person's movement data, etc., included in the output data output from the human detection learned model described above. For example, the control unit 210 determines that a person who satisfies the detection condition has been detected when the detected location included in the output data (i.e., the smallest rectangular area that includes the detected person) is within the detection area of ​​the detection condition. For example, the control unit 210 determines that a person who satisfies the detection condition has not been detected when the detected location included in the output data (i.e., the smallest rectangular area that includes the detected person) is not within the detection area of ​​the detection condition. Also, for example, when the detected location included in the output data (i.e., the smallest rectangular area that includes the detected person) passes through the detection line of the detection condition, the control unit 210 determines that a person that satisfies the detection condition has been detected together with the passing direction (IN / OUT, etc.). For example, when the detected location included in the output data (i.e., the smallest rectangular area that includes the detected person) does not pass through the detection line of the detection condition, the control unit 210 determines that a person that satisfies the detection condition has not been detected.

[0071] If control unit 210 determines that the detected person satisfies the detection condition, control unit 210 advances the process to step S1220. If control unit 210 determines that the detected person does not satisfy the detection condition, control unit 210 repeats step S1210.

[0072] In step S1220, the control unit 210 determines whether the detected person is a registered person. The control unit 210 determines whether the detected person is a registered person based on the face image of the person included in the output data output from the person detection trained model and the person image and staff image stored in the person image 840 of the registered staff table 800. For example, the control unit 210 uses a feature point detection algorithm such as SIFT (Scale-Invariant Feature Transform) or SURF (Speed ​​Up Robust Features) to extract feature points from the face image and determine the match by comparing the positions and shapes of the feature points. Alternatively, the control unit 210 inputs the face image to the trained model. This trained model is a trained model trained with the face image as input data and the feature amount of the face image as output data. The control unit 210 may determine the match of the face image by comparing the output feature amounts. If the control unit 210 determines that there is a match as a result of the consistency check, it determines that the detected person is a registered person, and if it determines that there is no match, it determines that the detected person is not a registered person. If control unit 210 determines that the detected person is a registered person, it proceeds to step S1230, and if control unit 210 determines that the detected person is not a registered person, it proceeds to step S1240.

[0073] In step S1230, the control unit 210 judges whether or not the department of the registered person determined to be the same as the detected person matches (corresponds to) the department of the corresponding surveillance camera 160. This match (correspondence) includes not only a perfect match but also a partial match and similarity. The control unit 210 acquires the department of the registered staff member from the corresponding person department 830 based on the person ID of the registered person (registered staff member) determined to be the same as the detected person. The control unit 210 also acquires the department of the surveillance camera 160 from the corresponding camera department 530 based on the camera ID of the surveillance camera 160 that captured the video in which the person was detected. The control unit 210 judges that the attributes are the same if the departments are the same, and judges that the attributes are different if the departments are not the same. If control unit 210 determines that the attributes are the same, it returns the process to step S1200, and if control unit 210 determines that the attributes are not the same, it proceeds to step S1240.

[0074] In step S1240, the control unit 210 stores predetermined information in the detection result table 1300.

[0075] 13 is a diagram showing an example of a detection result table 1300. This table shows a list of data stored in S1240 or S1250 when the result in S1210 is YES. The detection result table 1300 includes the following data items: detection ID 1310, camera ID 1315, camera name 1320, camera department 1325, detection date and time 1330, detection type 1335, detection name (area / line) 1340, stay time 1345, passage direction 1350, registered person 1355, person ID 1360, person name 1365, and person department 1370. In the detection ID 1310, identification information for identifying the detection, which is assigned by the control unit 210 when a person is detected from the video, is stored. In the camera ID 1315, identification information for identifying the surveillance camera 160 capturing the video in which a person was detected is stored. In the camera name 1320, the name of the surveillance camera 160 (camera name) is stored. In the camera category 1325, a camera category (camera category) is set. The detection date and time 1330 stores the detection date and time when a person was detected by the surveillance camera 160. The detection type 1335 stores information on whether the person was detected in the detection area or on the passing line. In the detection name (area / line) 1340, the name of the detection area or the name of the passing line set on the detection condition setting screen 1100 or the like is stored. The staying time 1345 stores the time that a person stayed in the detection area. The staying time is stored when a person is detected in the detection area. This information is stored when the detection type 1335 is an area, and when it is not (for example, when it is a line), information indicating a blank such as "-" is stored. Passing direction 1350 stores information on whether a person entered or exited the passing line. The passing direction is stored when a person entered / exited the passing line. This information is stored when detection type 1335 is a line, and when not (for example, when it is an area), information indicating a blank such as "-" is stored. The registered person 1355 stores information on whether the detected person is a person registered in the staff registration ("◯" or "X"). The person ID 1360 stores identification information for identifying a person (registered staff member). The person name 1365 stores the name of the staff member. The personnel department 1370 stores the department of the staff member. If the registered person 1355 is "◯", that is, if the detected person is a registered staff member, then these are stored in the person ID 1360, person name 1365, and person department 1370. On the other hand, if the registered person 1355 is "×", then information indicating a blank such as "-" is stored. In addition, for example, an item (column) called "department match" (not shown) may be provided, and when the registered person 1355 is "O", "O" may be stored if the camera department 1325 and the person department 1370 correspond (match / similar) based on the correspondence between the two, and "X" may be stored if the two do not correspond (match / similar).

[0076] The detection result records stored in the detection result table 1300 in step S1240 are records relating to detected persons who are not registered persons (registered staff), and records relating to persons who are registered persons (registered staff) but whose camera department and person department are different. In step S1260, the control unit 210 counts up the number of records related to detected persons who are not registered persons (registered staff) and records related to persons who are registered persons (registered staff) but whose camera category and person category are different in the detection result records of the detection result table 1300. This counting is performed for each detection condition (area / line) of each camera. When the detection condition is a line, the IN / OUT of the same line may be counted separately. The control unit 210 may count up the number of records such as data for a preset period (data for the most recent month). The control unit 210 may also count up the number of records such as data for a preset period (data for the most recent month) for each detection name. The control unit 210 stores the counted-up result in a predetermined storage area such as the storage unit 220. The counted number is stored in association with information on the camera / detection condition (area / line). If the detection condition is a line, the IN / OUT of the same line may be stored separately. The counted-up result can be said to be the number of people detected in the area / line for each camera. Although the counting in step S1260 has been described as being executed each time step S1240 is performed, it may be counted all at once later. For example, when a user instructs creation of a graph, which will be described later, it may be possible to pick up detection result records in detection result table 1300 in Fig. 13 that correspond to the report period (measurement period), device (camera ID / camera name / camera department), and detection condition (area or line) specified in the instruction, and count the number of them.

[0077] In step S1250, control unit 210 stores predetermined information in detection result table 1300. The detection result record stored in the detection result table 1300 in step S1250 is a record relating to a registered person (registered staff member) whose camera category and person category are the same.

[0078] (2-4) Output processing of measurement results In response to a request from the client device 120 , the control unit 210 generates a counting result list screen and transmits it to the client device 120 . FIG. 14 is a diagram showing an example of a counting result list screen 1400. As shown in FIG. A list of count-up result data for each predetermined period (measurement period) is displayed on the count result list screen 1400. This data is created when the measurement data (CSV) creation button 1410 is selected. When the measurement data (CSV) creation button 1410 is selected, the control unit 210 generates a measurement data (CSV) creation screen 1500 as shown in Fig. 15. Fig. 15 is a diagram showing an example of the measurement data (CSV) creation screen 1500. CSV is an abbreviation for Comma Separated Values. Then, the control unit 210 transmits the measurement data (CSV) creation screen 1500 to the client device 120. The measurement data (CSV) creation screen 1500 is a screen for creating a CSV file of the measurement data of the data.

[0079] In the measurement data (CSV) creation screen 1500, the manager of the store where the surveillance camera 160 is installed can set various information such as the data name (file name), report period (measurement period), device (camera ID / camera name / camera department). Here, regarding the device setting, the camera can be set individually by the camera ID or camera name, and multiple devices can be set at once by attribute unit by the group setting of the department described later. When the measurement data (CSV) creation screen 1500 accepts the setting of various information from the user and the selection of the button to create, the control unit 210 creates a CSV file of the measurement data with the set data name. As a result, the measurement data (CSV) is created for the detection result in the video captured by the desired camera in the desired measurement period. As an image, for example, the data stored in the detection result table 1300 shown in FIG. 13 is narrowed down to the data corresponding to the device (camera ID / camera name / camera department) for which the camera ID 1315 / camera name 1320 / camera department 1325 is set and the data corresponding to the report period (measurement period) for which the detection date and time 1330 is set. The control unit 210 may then enable the client device 120 to download the created CSV file. As a method of downloading, for example, a list of the measurement data (CSV) shown in Fig. 14 may be displayed, and when the user selects one or more of them and presses a download button (not shown), the selection is accepted and the corresponding measurement data (CSV) is downloaded (one form of output). In other words, the control unit 210 outputs the measurement results to a file in CSV format. This allows users to use this measurement data (CSV) to narrow down various information as desired, display it in graphs, etc., allowing them to perform the data analysis they desire.

[0080] As another example of output of the measurement result, the control unit 210 may generate a screen including the result of counting up and send the generated screen to the client device that made the request. The screen includes a graph of the result of counting up. As a method of creating a graph, for example, a graph creation button 1420 (not shown) is provided in FIG. 14, and when the user presses it, a graph creation screen is displayed as in FIG. 15, where the detection conditions (area and line) can be set in addition to various information such as the data name (file name), report period (measurement period), and device (camera ID / camera name / camera department). This allows a graph to be created for the results of detection under the desired detection conditions (area and line) in the video captured by the desired camera during the desired measurement period. The image is that, from the numbers counted in S1260, information on the target camera / measurement period is picked up, and, for example, the horizontal axis is time / the vertical axis is count number, and the count number under the target detection conditions (area and line) is expressed in chronological order. When the detection condition is a line, the IN / OUT of the same line may be separately represented. In addition, a plurality of different detection conditions for the same camera, or even detection results for a plurality of different cameras, may be displayed superimposed on a single graph. The created graph may be made downloadable like the measurement data (CSV), or may be displayed on the UI of the client device 120. That is, the control unit 210 may display the measurement results in a graph.

[0081] As described above, according to the first embodiment, it is possible to easily find a person from a video and register a face image of the person of interest in the information processing system 1000. Furthermore, according to the first embodiment, it is possible to detect and output a video of a person with a face image selected from the registered face images.

[0082] As described above, according to the first embodiment, it is possible to count the number of people accurately. For example, it is possible to count the number of people flexibly, such as not counting staff from the same store as the number of people, but counting staff from different stores as customers.

[0083] (Variation 1) Modification 1 of embodiment 1 will be described. Modification 1 is included in embodiment 1, and is not an embodiment different from embodiment 1. Configurations and processes not described in modification 1 are similar to those of embodiment 1 described above.

[0084] The control unit 210 of the first modification may generate a detection group setting screen for setting a detection group in response to a request from the client device 120 or the like, and transmit the generated detection group setting screen to the client device 120 or the like. 16 is a diagram showing an example of a detection group setting screen 1600. The detection group setting screen 1600 is a screen for collating multiple devices to determine whether or not the same person exists when multiple surveillance cameras 160 are installed in one department (in the example of the specification, the unit is store, assuming a company that operates multiple stores, but other than that, the unit may be country / region or the like in the case of a company that operates in multiple countries or regions, or brand / company or the like in the case of a company that has multiple brands or group companies, etc.).

[0085] When one department is selected in FIG. 16, the control unit 210 generates a screen including a list of one or more security cameras 160 installed in the selected department, and transmits the screen to the client device 120 or the like. 17 is a diagram showing an example of a screen 1700 for selecting the surveillance cameras 160 to be grouped. Through this screen, the user can edit (add / delete, etc.) the cameras corresponding to the department, and when an instruction is received from the user, the information (information associating the camera ID 510 and camera name 520 with the camera department 530 in FIG. 5) is saved / updated.

[0086] According to the first modification, the surveillance cameras 160 related to the department can be grouped. When setting a person category (input area 720 in FIG. 7), a list of grouped categories may be displayed, allowing the user to select a group from the list, and when an instruction is received from the user, the information (information associating person ID 810 and person name 820 with person category 830 in FIG. 8) may be saved / updated. This allows the person category to be set easily and reliably. In addition, when selecting a device to create measurement data (CSV) (the device addition screen at the bottom right of Figure 15), a list of grouped departments can be displayed, allowing the user to select a group from that list, and when instructions are received from the user, measurement data (CSV) can be created for that information (images captured by the camera corresponding to the selected department). This makes it easy and reliable to select a camera when creating measurement data (CSV).

[0087] <Additional Notes> The present invention may be provided in the following manner. (Appendix 1) An information processing system that analyzes an image captured by a camera and outputs data, For each camera, set the camera attributes, For each person, the attributes of the person are registered, When a predetermined detection condition is satisfied in the video, detection information regarding the detection is stored including attributes of the camera related to the video and attributes of the person related to the video; outputting the detection information; Information processing system. (Appendix 2) An information processing system that analyzes an image captured by a camera and outputs data, For each camera, set the camera attributes, For each person, the attributes of the person are registered, When a predetermined detection condition is satisfied in the video, detection information regarding the detection is stored, including information indicating whether an attribute of a camera related to the video corresponds to an attribute of a person related to the video; outputting the detection information; Information processing system. (Appendix 3) An information processing system that analyzes an image captured by a camera and outputs data, For each camera, set the camera attributes, For each person, the attributes of the person are registered, When a predetermined detection condition is satisfied in the video, counting the number of people, except when the person in the video is a registered person and the attributes of the person correspond to the attributes of the camera in the video; outputting the result of said counting; Information processing system. (Appendix 4) An information processing system according to any one of claims 1 to 3, Setting the detection conditions; Information processing system. (Appendix 5) An information processing system according to any one of claims 1 to 3, The attribute is any one of a store, a store floor, and an area related to the store. Information processing system. (Appendix 6) 6. The information processing system according to claim 5, Grouping said attributes; Information processing system. (Appendix 7) 7. An information processing system according to any one of claims 1 to 6, The detection condition includes information about an area of ​​the image. Information processing system. (Appendix 8) 7. An information processing system according to any one of claims 1 to 6, The detection condition includes information about a line of the image. Information processing system. (Appendix 9) 4. An information processing system according to claim 3, The result of the count is output to a file in CSV format. Information processing system. (Appendix 10) 4. An information processing system according to claim 3, Displaying the results of said counting in a graph; Information processing system. (Appendix 11) An information processing method executed by an information processing system that analyzes an image captured by a camera and outputs data, comprising: For each camera, set the camera attributes, For each person, the attributes of the person are registered, When a predetermined detection condition is satisfied in the video, detection information regarding the detection is stored including attributes of the camera related to the video and attributes of the person related to the video; outputting the detection information; Information processing methods. (Appendix 12) An information processing method executed by an information processing system that analyzes an image captured by a camera and outputs data, comprising: For each camera, set the camera attributes, For each person, the attributes of the person are registered, When a predetermined detection condition is satisfied in the video, detection information regarding the detection is stored, including information indicating whether an attribute of a camera related to the video corresponds to an attribute of a person related to the video; outputting the detection information; Information processing methods. (Appendix 13) An information processing method executed by an information processing system that analyzes an image captured by a camera and outputs data, comprising: For each camera, set the camera attributes, For each person, the attributes of the person are registered, When a predetermined detection condition is satisfied in the video, counting the number of people, except when the person in the video is a registered person and the attributes of the person correspond to the attributes of the camera in the video; outputting the result of said counting; Information processing methods. (Appendix 14) A program, Computer, A program for causing the information processing system according to any one of claims 1 to 10 to function as such.

[0088] Although various embodiments of the present invention have been described, these are presented as examples and are not intended to limit the scope of the invention. The new embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. The embodiments and modifications of the embodiments are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims.

[0089] For example, part of the processing of the server device 100 described above may be executed by the client device 110 and / or the client device 120. Also, part of the processing of the server device 100 described above may be executed by the surveillance camera 160. [Explanation of symbols]

[0090] 100: Server device 110: Client device 150: Network 160: Surveillance camera 210: Control section 220: Storage section 230: Communications Department 1000: Information processing systems

Claims

1. An information processing system that analyzes an image captured by a camera and outputs data, For each camera, set the camera attributes, The attribute of the camera indicates a store or an area including a plurality of stores; For each person, the attributes of the person are registered, The attribute of the person indicates a store or an area including a plurality of stores; When a predetermined detection condition is satisfied in the video, detection information regarding the detection is stored including attributes of the camera related to the video and attributes of the person related to the video; outputting the detection information; Information processing system.

2. An information processing system that analyzes an image captured by a camera and outputs data, For each camera, set the camera attributes, The attribute of the camera indicates a store or an area including a plurality of stores; For each person, the attributes of the person are registered, The attribute of the person indicates a store or an area including a plurality of stores; When a predetermined detection condition is satisfied in the video, detection information regarding the detection is stored, including information indicating whether an attribute of a camera related to the video corresponds to an attribute of a person related to the video; outputting the detection information; Information processing system.

3. An information processing system that analyzes an image captured by a camera and outputs data, For each camera, set the camera attributes, The attribute of the camera indicates a store or an area including a plurality of stores; For each person, the attributes of the person are registered, The attribute of the person indicates a store or an area including a plurality of stores; When a predetermined detection condition is satisfied in the video, counting the number of people, except when the person in the video is a registered person and the attributes of the person correspond to the attributes of the camera in the video; outputting the result of said counting; Information processing system.

4. An information processing system according to any one of claims 1 to 3, Setting the detection conditions; Information processing system.

5. 2. The information processing system according to claim 1, Grouping said attributes; Information processing system.

6. An information processing system according to any one of claims 1 to 3, The detection condition includes information about an area of ​​the image. Information processing system.

7. An information processing system according to any one of claims 1 to 3, The detection condition includes information about a line of the image. Information processing system.

8. 4. The information processing system according to claim 3, outputting the counting result to a file in CSV format; Information processing system.

9. 4. The information processing system according to claim 3, Displaying the results of said counting in a graph; Information processing system.

10. An information processing method executed by an information processing system that analyzes an image captured by a camera and outputs data, comprising: For each camera, set the camera attributes, The attribute of the camera indicates a store or an area including a plurality of stores; For each person, the attributes of the person are registered, The attribute of the person indicates a store or an area including a plurality of stores; When a predetermined detection condition is satisfied in the video, detection information regarding the detection is stored including attributes of the camera related to the video and attributes of the person related to the video; outputting the detection information; Information processing methods.

11. An information processing method executed by an information processing system that analyzes an image captured by a camera and outputs data, comprising: For each camera, set the camera attributes, The attribute of the camera indicates a store or an area including a plurality of stores; For each person, the attributes of the person are registered, The attribute of the person indicates a store or an area including a plurality of stores; When a predetermined detection condition is satisfied in the video, detection information regarding the detection is stored, including information indicating whether an attribute of a camera related to the video corresponds to an attribute of a person related to the video; outputting the detection information; Information processing methods.

12. An information processing method executed by an information processing system that analyzes an image captured by a camera and outputs data, comprising: For each camera, set the camera attributes, The attribute of the camera indicates a store or an area including a plurality of stores; For each person, the attributes of the person are registered, The attribute of the person indicates a store or an area including a plurality of stores; When a predetermined detection condition is satisfied in the video, counting the number of people, except when the person in the video is a registered person and the attributes of the person correspond to the attributes of the camera in the video; outputting the result of said counting; Information processing methods.

13. A program, Computer, A program for causing the information processing system according to any one of claims 1 to 3 to function as such.

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