JP2021103420A - Guiding system, server, program, and service providing method - Google Patents

Abstract

To provide a guiding system, a server, a program, and a service providing method capable of guiding a staying person by urging the person to move.SOLUTION: A guiding system includes image sensors 3(3-0 to 3-n) for sensing a person, and a cloud server 200 communicative with the image sensors 3(3-0 to 3-n). The image sensors 3(3-0 to 3-n) include an analysis unit. The analysis unit generates sensor data containing positions of persons and staying time by analyzing data obtained by imaging a prescribed area with an imaging element. The server 200 includes an acquisition unit and a recommendation information generation unit. The acquisition unit acquires sensor data from the image sensors 3(3-0 to 3-n). The recommendation information generation unit generates recommendation information for urging a person to move on the basis of the positions of persons and staying time contained in the acquired sensor data.SELECTED DRAWING: Figure 1

Description

Embodiments of the present invention relate to guidance systems, servers, programs and service provision methods.

In recent years, motion sensors are equipped with a CPU (Central Processing Unit) and memory, and can be said to be an embedded computer with a lens. It also has advanced processing functions, for example, an image sensor can analyze captured image data and calculate, for example, the presence / absence of humans or the number of people.

Japanese Patent No. 6151085

By the way, in a station yard, a shopping mall, a food court, etc., if a person sits or stands in a certain place and stays for a long time, it may become an obstacle when another person moves or wants to stay in the same place for a while. The challenge was how to move people who stay for a long time smartly.

Therefore, the purpose is to provide a guidance system, a server, a program, and a service providing method for encouraging the staying person to move.

According to the embodiment, the guidance system includes a motion sensor that detects a person and a server that can communicate with the motion sensor. The motion sensor includes an analysis unit. The analysis unit analyzes the data obtained by imaging a predetermined area with an image sensor, and generates sensor data including the position of a person and the staying time. The server includes an acquisition unit and a recommendation information generation unit. The acquisition unit acquires sensor data from the motion sensor. The recommendation information generation unit generates recommendation information that encourages the movement of a person based on the position and staying time of the person included in the acquired sensor data.

FIG. 1 is a diagram showing an example of a guidance system according to an embodiment. FIG. 2 is a block diagram showing an example of the image sensor 3. FIG. 3 is a diagram showing an example of image data obtained by the image sensor 3. FIG. 4 is a diagram showing an example of sensing items of the image sensor 3. FIG. 5 is a block diagram showing an example of the cloud server 200. FIG. 6 is a diagram showing an example of congestion information. FIG. 7 is a diagram showing an example of store information. FIG. 8 is a diagram showing an example of weather information. FIG. 9 is a diagram showing an example of information managed by the IC card database 240f. FIG. 10 is a functional block diagram showing an example of the tablet 20. FIG. 11 is a sequence diagram showing a procedure in which the cloud server 200 acquires sensor data from the image sensor 3. FIG. 12 is a flowchart showing an example of a processing procedure related to store visitor registration by the cloud server 200. FIG. 13 is a flowchart showing an example of the processing procedure of the cloud server 200. FIG. 14 is a schematic diagram showing an example of recommendation information. FIG. 15 is a schematic diagram showing an example of recommendation information. FIG. 16 is a flowchart showing an example of a processing procedure related to prompting to leave. FIG. 17 is a flowchart showing another example of the processing procedure related to the store visitor registration by the cloud server 200. FIG. 18 is a flowchart showing another example of the processing procedure of the cloud server 200.

An image sensor, which is a motion sensor, can acquire a variety of information as compared with a sensor, an infrared sensor, or the like. If a fisheye lens, an ultra-wide-angle lens, or the like is used, the area that can be photographed by one image sensor can be enlarged, and the distortion of the image can be corrected by calculation. An image sensor having a function of setting a mask for an area in the field of view that is not desired to be sensed and a learning function is also known. The image sensor according to the embodiment can obtain, for example, human information or environmental information by taking an image captured in the field of view and processing the acquired image data.

Human information is information about humans in the target space, and examples thereof include the existence or absence (presence / absence) of a person, the number of people, the behavior of a person, and the amount of activity of a person. An object (article) attached to a person, that is, the presence or absence of luggage is also an example of human information. The environmental information is information about the environment of the space to be controlled (target space), and examples thereof include a store illuminance distribution and a temperature distribution.

Human information and environmental information can be calculated for each target space. Alternatively, human information and environmental information can be calculated for each small area (area) in which the target space is divided into a plurality of areas. In this embodiment, attention is paid particularly to the presence / absence of human beings and the seated / vacant seats in the store.

FIG. 1 is a diagram showing an example of a guidance system according to an embodiment. In FIG. 1, a plurality of image sensors 3 (3-0 to 3-n) are arranged on the ceiling of the store T, for example, in a form overlooking the inside of the store. The image sensor 3 captures a plurality of tables and seats (chairs) arranged in the visitor area in the field of view. The image sensor 3 captures an image captured in the field of view and generates image data.

In particular, the image sensor 3-0 is installed near the entrance of the store T or the entrance gate, and images the visitor from the front direction to acquire face image data or whole body image data.

Each image sensor 3 is connected to, for example, a gateway (GW) 7 arranged in a control room via a signal line 100. EtherCAT (registered trademark), BACnet (registered trademark), TCP / IP (Transmission Control Protocol / Internet Protocol), UDP / IP, and the like can be applied to the protocol of the signal line 100.

The gateway 7 is connected to the in-store network 500. Building Automation and Control Networking protocol (BACnet (registered trademark)), DALI, ZigBee (registered trademark), ECHONET Lite (registered trademark), and the like can be applied to the communication protocol of the in-store network 500.

The management server 400, the store server 5, and the firewall 6 are connected to the in-store network 500. The firewall 6 is connected to the cloud server 200 of the cloud 600 via, for example, a dedicated line network 300 formed on the Internet.

With the above configuration, the guidance system can safely access the cloud server 200 of the cloud 600, the database 700a of the data center 700, and the like, and use various resources. The service provided by the cooperation between the image sensor 3 and the cloud server 200 will be described below.

The guidance system according to the embodiment can also be linked with various systems accessible via the Internet or the like and a database, and can use data obtained from various information sources. Examples of the information source include a reservation system and an ordering system for each store, a weather data server that distributes weather data and weather information, and a server of a telecommunications carrier.

FIG. 2 is a block diagram showing an example of the image sensor 3. The image sensor 3 includes a camera unit 31, a storage unit 32, a processor 33, and a communication unit 34. These are connected to each other via an internal bus 35.

The camera unit 31 includes a fisheye lens 31a, an aperture mechanism 31b, an image sensor (image sensor) 31c, and a register 30. The fisheye lens 31a forms an image in the field of view on the image sensor 31c. In the embodiment, the fisheye lens 31a captures the visitor area of the store as a field of view, and forms an image of the visitor area on the image sensor 31c. The visitor area is usually divided into a plurality of areas, and each area is imaged by one image sensor, and the image data obtained by the plurality of image sensors is integrated to cover the entire area of the visitor area. You may try to do it.

The image sensor 31c is an image sensor typified by CMOS (complementary metal oxide semiconductor), and generates a video signal having a frame rate of, for example, 30 frames per second. This video signal is digitally coded, and image data obtained by imaging the visitor area with the image sensor 31c is generated. The image data may include human face image data. The amount of light incident on the image sensor 31c is adjusted by the aperture mechanism 31b.

The register 30 stores the camera information 30a. The camera information 30a is information about the camera unit 31, such as the state of the auto gain control function, the gain value, and the exposure time, or information about the image sensor 3 itself.

The storage unit 32 is a semiconductor memory such as an SDRAM (Synchronous Dynamic RAM) or an EPROM (Erasable Programmable ROM). The storage unit 32 uniquely distinguishes the image data 32a including the face image data acquired by the camera unit 31, the program 32b for causing the processor 33 to execute various functions related to the embodiment, and the image sensor. The sensor ID (IDentification) 32c and the dictionary data 32d are stored.

The dictionary data 32d is data similar to template data prepared according to detection items (people, objects, men, women, etc.) in the image sensor 3. For example, the chair is detected from the image data by performing the template matching process using the dictionary data 32d prepared for the detection of the "chair". Various dictionary data 32d are prepared in advance in order to make it possible to distinguish the accuracy, the type of chair (office chair, sofa, sitting chair, etc.) and the color. In addition, dictionary data for detecting wheelchairs, strollers, children, carts, white cane users, wandering elderly people, suspicious persons, lost children, persons who have been late to escape, etc. can be created. As an example of the creation method, a machine learning framework such as a support vector machine or a Boltzmann machine can be used.

The processor 33 realizes various functions described in the embodiment by loading and executing the program stored in the storage unit 32. The processor 33 is, for example, an LSI (Large Scale Integration) including a multi-core CPU (Central Processing Unit) and tuned for executing image processing at high speed. The processor 15 can also be configured by FPGA (Field Programmable Gate Array) or the like. MPU (Micro Processing Unit) is also one of the processors.
The communication unit 34 can connect to the signal line 100 and mediates the exchange of data with the gateway 7, the management server 400, the store server 5, and the cloud 600.

By the way, the processor 33 includes a detection unit 33a, a feature amount calculation unit 33b, an analysis unit 33c, and a learning unit 33d as processing functions according to the embodiment. The detection unit 33a, the feature amount calculation unit 33b, the analysis unit 33c, and the learning unit 33d are, for example, generated in a process in which the processor 33 executes arithmetic processing according to the program 32b loaded in the register of the processor 33. is there.

The detection unit 33a detects an object in the visual field using the dictionary data 32d. That is, the detection unit 33a detects an object in the target space from the image data 32a by using the dictionary data set prepared for each detection item.

The feature amount calculation unit 33b processes the image data 32a stored in the storage unit 32 by a predetermined algorithm to extract the feature amount related to a person or an object in the visual field. For example, it is possible to calculate the motion feature amount by tracing the change in the brightness of each frame of the image data pixel by pixel and analyzing the time series. Histograms of oriented gradients (HOG) Features such as features, contrast, resolution, signal-to-noise ratio, and color tone are known. Further, a co-occurrence histogram (Co-occurrence HOG: Co-HOG) feature quantity, a Haar-Like feature quantity, and the like are also known as feature quantities.

The analysis unit 33c analyzes the image data 32a and generates sensor data including the position of the seated person, the attribute information, and the staying time. That is, the analysis unit 33c has human information (position of person, presence / absence of person, number of people, behavior of person, amount of activity of person, presence / absence of luggage, etc.) based on the feature amount obtained by, for example, the feature amount calculation unit 33b. And environmental information (illuminance distribution, temperature distribution, etc.) are calculated. In the embodiment, [human cell position], [absence], [stay time], and [walking retention] are assumed as sensing items. The analysis unit 33c can further sense more detailed attribute information such as male, female, young, middle-aged, and old.

In addition, the analysis unit 33c refers to human information, environmental information, feature quantities, and the like, and acquires information on people and goods in the store, for example, by rule-based recognition or machine learning recognition. For example, it is possible to detect a person's behavior (customer's ordering behavior, clerk's tidying up behavior, etc.) by a pattern recognition technology using a motion feature amount.

The learning unit 33d performs learning based on the output of the image data 32a and the feature amount calculation unit 33b in order to improve the correct answer rate of the analysis result by the analysis unit 33c.

FIG. 3 is a diagram showing an example of image data obtained by the image sensor 3. The frame of this image data is divided into 7 cells in the (x) axis direction and the (y) axis direction, respectively, and is divided into 7 × 7 = 49 cells. Four tables and four seats are captured in the field of view of the central image sensor 3. The image sensor 3 detects a person in the cells (x, y) = (2,2), (2,6), (6,6), (1,4), and is shown in FIG. 4 as the person cell position. Obtain sensor data.

As shown in FIG. 4, the presence flag 1 is set in the cells (2, 2), (2, 6), and (6, 6) according to the environment of FIG. The presence flag is 0 for cells (1 and 4), indicating that (moving) has been detected. Collectively, sensor data of [absence] = (1,1,1,0) can be obtained. For each cell in which a person is detected, for example, (30, 30, 40, 00) is detected as a [stay (attendance) time] item. The unit is (minutes). (0, 0, 0, 1) is detected in each cell as [walking retention].

Further, the attributes of the person at each cell position (x, y) = (2,2), (2,6), (6,6), (1,4) are specified as, for example, a man, a woman, a woman, and a man. Then, identification information (person ID) for distinguishing each is given as, for example, 001,002,003,004.

This will be described in more detail. The sensor data calculated by the analysis unit 33c includes (1) presence / absence of people, (2) number of people, (3) amount of activity of people, (4) illuminance, (5) pedestrian retention status (walking retention), and And so on.

(1) Detection of presence / absence of a person (presence / absence of a person) For example, the analysis unit 33c may detect the presence / absence of a person by identifying a person area in the image based on the difference between the images. Alternatively, the presence or absence of a person can be detected by applying the person recognition technology. As the difference between the images, a difference from the background image, a difference between temporally continuous images (difference between frames), and the like may be used.

(2) Estimating the number of people For example, the analysis unit 33c identifies a person area in the image by the same method as in (1), processes the image data of the identified person area, and performs image processing on the person existing in the target space. You may estimate the number of people.

(3) Estimating the amount of human activity For example, the analysis unit 33c may extract a movement region using an inter-frame difference or the like, and estimate the amount of activity based on the size, shape, or the like of the extracted movement region. .. For example, the amount of activity may be estimated at the stage of "none", "small", "medium", "large", etc., or may be estimated as an index value such as METs (Metabolic Equivalents). The analysis unit 33c may extract a motion region for each subspace and estimate the amount of activity for each subspace.

(4) Illuminance estimation For example, the analysis unit 33c may estimate the illuminance of the target space (or each subspace) based on the image and the imaging set values (for example, gain, exposure time, etc.) of the camera unit 31. Good.

(5) Detection of gait retention For example, the analysis unit 33c may extract a movement region using an inter-frame difference or the like, and detect gait retention based on the size, shape, or the like of the extracted movement region.

The image sensor 3 repeatedly generates sensor data as shown in FIG. The sensor data is transmitted from the image sensor 3 to the cloud server 200 via the gateway 7.

FIG. 5 is a block diagram showing an example of the cloud server 200. The cloud server 200 is a computer including a processor 210 such as a CPU and an MPU, a ROM (Read Only Memory) 220, and a RAM (Random Access Memory) 230. The cloud server 200 further includes a storage unit 240 such as a hard disk drive (HDD), an optical media drive 260, and a communication unit 270.

The ROM 220 stores basic programs such as BIOS (Basic Input Output System) and UEFI (Unified Extensible Firmware Interface), and various setting data. The RAM 230 temporarily stores programs and data loaded from the storage unit 240.

The optical media drive 260 reads digital data recorded on a recording medium such as a CD-ROM 280. Various programs executed on the cloud server 200 are recorded and distributed on, for example, a CD-ROM 280. The program stored in the CD-ROM 280 is read by the optical media drive 260 and installed in the storage unit 240.

The communication unit 270 controls communication with the image sensor system via the leased line network 300 and the firewall 6.

The storage unit 240 stores the program 240a executed by the processor 210, the sensor data 240b, the congestion information 240c, the store information 240d, the weather information 240e, and the IC card database 240f. The sensor data 240b is acquired from the image sensor 3. The congestion information 240c, the store information 240d, and the IC card database 240f are acquired from, for example, the store server 5, or calculated by the cloud server 200 based on the sensor data 240b. The weather information 240e is acquired by accessing the weather data server.

FIG. 6 is a diagram showing an example of congestion information 240c. The congestion information is information that is managed by associating the current number of people with the set value of the degree of congestion for each position (visitor area) in the store. The positions are associated with, for example, different food courts (S1 to S5), and a congestion degree setting value is set as a threshold value for measuring the congestion degree for each position. If the congestion degree setting value = 3 is set, that is, if the number of people is 3 or more, the position is considered to be congested.

FIG. 7 is a diagram showing an example of store information 240d. The store information is information managed for each store (T1 to T5) in association with the genre of the products handled, the presence or absence of a sale at the present time, and the sale time.

FIG. 8 is a diagram showing an example of the weather information 240e. The weather information is an example of information outside the store, and is information in which the weather, temperature, and humidity are managed in association with the date and time. For example, during the rainy season, people tend to stay longer in stores to avoid rain. By referring to the weather information and knowing that it is sunny, you can signage it and make the customer feel like "let's go out ...". As a result, customers can be casually left.

FIG. 9 is a diagram showing an example of information managed by the IC card database 240f. The IC card database 240f is information that is managed in association with attributes, preferences (stores), preferences (outside the store), and presence / absence of entry for each person ID that can be uniquely distinguished by an IC card or the like. In short, in the IC card database 240f, preference information (for each ID / attribute) indicating a person's preference is managed.

For example, it can be seen that the person sensed with ID = 001 is a man, prefers food in the store, and prefers sunny information outside the store, and has already entered the store. This kind of database can be generated by analyzing the Web access history by a method such as collaborative filtering.

The processor 210 executes an OS (Operating System) and various programs. By the way, the processor 210 includes an acquisition unit 210a, an identification unit 210d, a recommendation information generation unit 210b, and a presentation unit 210c as processing functions according to the embodiment. In the acquisition unit 210a, the identification unit 210d, the recommendation information generation unit 210b, and the presentation unit 210c, the program 240a of the storage unit 240 is loaded into the RAM 230, and the processor 210 executes arithmetic processing as the program progresses. It can be understood as a process that is generated. That is, the program 240a is a program for operating the cloud server 200, and includes an instruction for operating the cloud server 200 as an acquisition unit 210a, an identification unit 210d, a recommendation information generation unit 210b, and a presentation unit 210c. ..

The acquisition unit 210a acquires sensor data including the position and staying time of the seated person from the image sensor 3. In addition, the acquisition unit 210a acquires congestion information and sale information. Further, the acquisition unit 210a accesses the weather data server to acquire the weather information of the geographical area including the target store.

The identification unit 210d identifies the individual seated person based on the face image data of each seated person included in the sensor data acquired from the image sensor 3.

The recommendation information generation unit 210b generates recommendation information for urging the seated person to leave the seat based on the position and staying time of the seated person included in the sensor data acquired from the image sensor 3. At that time, it is also possible to generate more appropriate recommendation information for the seated person by referring to the congestion information, the sale information, and the weather information. Further, in the embodiment, the attributes of the person and the individual are specified, and the tendency according to the attribute and the recommendation information for each individual are generated.
The presentation unit 210c presents the recommendation information calculated by the recommendation information generation unit 210b to the seated person via an electronic advertising medium such as digital signage or a Web page.

FIG. 10 is a functional block diagram showing an example of the tablet 20. The tablet 20 includes an operation unit 21, a display unit 22, a communication unit 23, an output unit 24, a control unit 25, and a memory 26. Each unit (operation unit 21, display unit 22, communication unit 23, output unit 24, control unit 25, and memory 26) is communicably connected by a bus 27 so that necessary data can be exchanged between each unit. It has become.

The operation unit 21 includes, for example, switches such as a power switch for turning on / off the power. The display unit 22 has a liquid crystal monitor with a touch panel and displays an image. Since the display unit 22 also has a touch panel function, it can also serve as a part of the functions of the operation unit 21.

The communication unit 23 includes a wireless communication unit that can be connected to a wireless communication facility (wireless LAN (Local Area Network) or the like) in the store T, a public cellular phone system, or the like. In the embodiment, it is assumed that various information is acquired by connecting to the wireless LAN in the store T.
The output unit 24 includes a speaker, an earphone jack, and the like, and reproduces and outputs analog voice and musical sound, and outputs an audio signal.

The memory 26 stores a program executed by the control unit 25, various data, a mobile OS (Operating System), and the like.
The control unit 25 includes a processor such as a CPU and controls the tablet 20. The CPU of the control unit 25 executes various processes according to the control program stored in the memory 26 and the installed application. In the embodiment, the control unit 25 reads the program stored in the memory 26 and executes the browser application (browser) 25a. The browser 25a displays the information on the Web page, the information unique to the store T, or the recommendation information transmitted from the cloud server 200 on the display unit 22. Next, the operation in the above configuration will be described.

FIG. 11 is a sequence diagram showing a procedure in which the cloud server 200 acquires sensor data from the image sensor 3. The image sensor 3 acquires image data (step S101) and stores it in the storage unit 32 (step S102). Then, the image sensor 3 performs image processing on the image data (step S103) to generate sensor data (step S104).

The gateway 7 acquires sensor data from the image sensor 3 (step S106) and stores the sensor data in a buffer memory or the like (step S107).
The cloud server 200 transmits, for example, a sensor data acquisition request to the gateway 7 at a constant polling cycle (step S301). The gateway 7 that has received the sensor data acquisition request (step S201) transmits the sensor data to the cloud server 200 (step S202). The cloud server 200 acquires the transmitted sensor data (step S203).

FIG. 12 is a flowchart showing an example of a processing procedure related to store visitor registration by the cloud server 200. When the entry of a person is detected (step S21), the cloud server 200 acquires the feature information indicating the facial feature and the physical feature of the shopkeeper from the sensor data transmitted from the image sensor 3 (step S22). ). Next, the cloud server 200 extracts attribute information such as male, female, or about age from the feature information (step S23) and registers it in the storage unit 240 (step S24). Through the procedure up to this point, the registration process is completed (step S25), and the person who has entered the store T is managed as the store entry state.

FIG. 13 is a flowchart showing an example of the processing procedure of the cloud server 200. In FIG. 11, the cloud server 200 refers to the sensor data from the image sensor 3 and monitors whether there is a seated person or a seated state for each seat (step S31). With reference to the congestion information 240c, if the degree of congestion is equal to or greater than the set value (YES in step S32), the cloud server 200 determines the staying time of the seat from the sensor data (step S33). If the staying time exceeds the default value (YES), the cloud server 200 creates recommendation information for prompting to leave and presents it to the seated person to prompt them to leave (step S34). After that, if it can be confirmed from the acquired sensor data that the user has left the seat (YES in step S35), the seat has been completed (step S42).

If the exit cannot be confirmed in step S35 (NO), the cloud server 200 determines whether or not there is store information matching the characteristics indicating the attribute of the person at that time (step S36). With reference to the store information 240d, for example, if it can be confirmed that the sale of the store T1 starts at 12:00 (YES), the cloud server 200 creates recommendation information indicating the food sale information at the store T1 and is seated. (Step S37).

FIG. 14 is a schematic diagram showing an example of recommendation information. In the embodiment, the recommendation information is displayed on the screen of the tablet 20 placed on the table, for example. As shown in FIG. 14, advertising information such as "Fresh food sale starts at 12:00 at the next store T1!" Is displayed on the tablet 20. After that, if it can be confirmed from the acquired sensor data that the user has left the seat (YES in step S8), the seat has been completed (step S42).

If the exit cannot be confirmed in step S38 (NO), the cloud server 200 refers to the weather information 240e and determines whether the weather at that time matches the characteristics indicating the attributes of the person (step S39). If the person has an attribute that prefers sunny weather, the (NO) processing procedure is completed up to this point if it is not sunny, and the state of being present is continued (step S43).

If the weather information is sunny, the cloud server 200 creates recommendation information that urges the person to go out and presents it to the occupants to urge them to leave (step S40).

FIG. 15 is a schematic diagram showing another example of the recommendation information. As shown in FIG. 15, advertising information such as "The weather is fine outside. Why don't you go to the nearby XX shopping street?" Is displayed on the tablet 20 to casually encourage you to go out. After that, if it can be confirmed from the acquired sensor data that the person has left the seat (YES in step S11), the seat is completed, and if NO, the person remains present (step S13).

FIG. 16 is a flowchart showing an example of a processing procedure related to prompting to leave. In FIG. 16, the cloud server 200 generates recommendation information (step S81). The recommendation information is generated as text data in HTML (HyperText Markup Language) format, for example. Next, the presentation unit 210c of the cloud server 200 notifies the target tablet 20 that the Web page for presenting the recommendation information has been updated (step S82). Upon receiving this notification, the tablet 20 requests the cloud server 200 to acquire the updated Web page.

The presentation unit 210c of the cloud server 200 waits for the arrival of the acquisition request of the Web page (step S83). Upon receiving the acquisition request (Yes), the presentation unit 210c of the cloud server 200 transmits HTML data including the recommendation information to the requesting tablet 20 (step S84). Upon receiving this, the browser 25a (FIG. 10) of the tablet 20 visualizes the HTML data and displays it on the display unit 22. By the above procedure, the recommendation information for prompting to leave is generated in the cloud server 200, and then immediately presented in the tablet 20.

As described above, according to the embodiment, the position, attribute, and staying time of the seated person in the store are passed to the cloud server 200 as the sensor data acquired by the image sensor 3. Based on these sensor data, the cloud server 200 determines the congestion status of the store, the staying time of the seated person, and the like. Based on the results, the signage device (tablet, etc.) on the desk of the seated person for a long time is displayed with the recommendation information for prompting the person to leave according to the attribute of the person.

With existing technology, it was not known to create and actively present recommendation information to encourage people to leave. On the other hand, in the embodiment, an advertising medium can be used to naturally encourage long-term seated persons to leave. From these facts, according to the embodiment, it becomes possible to provide a guidance system, a server, a program, and a service providing method capable of urging a resident person to leave the seat.

<Modification example>
FIG. 17 is a flowchart showing another example of the processing procedure related to the store visitor registration by the cloud server 200. In the above explanation, the attribute information of the shopkeeper is acquired from the sensor data and the recommendation information is created. In the modified example, by taking a step further and identifying the individual of the entrant, a more effective urge to leave the store is realized.

In FIG. 17, when a person enters the store (step S51), the cloud server 200 detects, for example, the touch of the IC card by the store entrant to a device (card sensor or the like) provided at the store entrance (step). S52). Alternatively, information similar to IC card information can be acquired by short-range wireless communication using a device such as a smartphone. The IC card information (personal ID, etc.) acquired in this way is registered in the IC card database 240f as a store entry record (step S53).

Then, the cloud server 200 registers the ID of the IC card database 240f in association with the ID of the corresponding person sensed by the image sensor (step S54). Through the procedure up to this point, the registration process is completed (step S55), and the person who has entered the store T is managed as the store entry state. That is, each store entrant is managed in association with a unique ID.

FIG. 18 is a flowchart showing another example of the processing procedure of the cloud server 200. In FIG. 18, the cloud server 200 refers to the sensor data from the image sensor 3 and monitors whether there is a seated person or a seated state for each seat (step S61). With reference to the congestion information 240c, if the degree of congestion is equal to or higher than the set value (YES in step S62), the cloud server 200 determines the staying time of the person (ID: xxx) staying at the seat from the sensor data. Determine (step S63). If the staying time exceeds the default value (YES), the cloud server 200 creates recommendation information for urging the person to leave the room and presents it to the person (ID: xxx) to urge the person to leave the room (step S64). After that, if it can be confirmed from the acquired sensor data that the person has left the seat (YES in step S65), the person has left the seat (step S72).

If the exit cannot be confirmed in step S65 (NO), the cloud server 200 determines whether or not there is store information that matches the characteristics of the person (ID: xxx) at that time (step S66). With reference to the store information 240d, for example, if it can be confirmed that the sale of the store T1 starts at 12:00 (YES), the cloud server 200 creates recommendation information indicating the food sale information at the store T1 and (ID). : Xxxx) to prompt the person to leave (step S67).

If the exit cannot be confirmed in step S68 (NO), the cloud server 200 refers to the weather information 240e and determines whether the weather at that time matches the characteristics of the person (ID: xxx) (step S69). ). If the person has an attribute that prefers sunny weather, the (NO) processing procedure is completed up to this point if it is not sunny, and the state of being present is continued (step S73). If the weather information is sunny, the cloud server 200 creates recommendation information for urging the person (ID: xxx) to go out, presents it to the occupants, and urges them to leave (step S70).

As described above, in the modified example, the individual of the entrant is specified based on the face image data, and the recommendation information is created according to the information (preference information) indicating the personal preference registered in advance. .. This makes it possible to more effectively encourage people to leave their seats in a way that is more in line with their personal tastes.

The present invention is not limited to the above embodiment. For example, it is possible to encourage the seated person to go out not only by the messages shown in FIGS. 14 and 15 but also by displaying the congestion status of the surrounding stores in detail.
Further, some or all of the functions provided in the cloud server 200 may be implemented in the gateway 7.

Although embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. This novel embodiment can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. This embodiment and its modifications are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

3 ... Image sensor, 5 ... Store server, 6 ... Firewall, 7 ... Gateway, 15 ... Processor, 20 ... Tablet, 21 ... Operation unit, 22 ... Display unit, 23 ... Communication unit, 24 ... Output unit, 25 ... Control Unit, 25a ... Browser, 26 ... Memory, 27 ... Bus, 30 ... Register, 30a ... Camera information, 31 ... Camera unit, 31a ... Fish-eye lens, 31b ... Aperture mechanism, 31c ... Imaging element, 32 ... Storage unit, 32a ... Image Data, 32b ... Program, 32d ... Dictionary data, 33 ... Processor, 33a ... Detection unit, 33b ... Feature calculation unit, 33c ... Analysis unit, 33d ... Learning unit, 34 ... Communication unit, 35 ... Internal bus, 100 ... Signal Line, 200 ... cloud server, 220 ... ROM, 230 ... RAM, 240 ... storage, 240a ... program, 240b ... sensor data, 240c ... congestion information, 240d ... store information, 240e ... weather information, 240f ... IC card database, 210 ... Processor, 210a ... Acquisition unit, 210b ... Recommendation information generation unit, 210c ... Presentation unit, 210d ... Identification unit, 260 ... Optical media drive, 270 ... Communication unit, 300 ... Dedicated line network, 400 ... Management server, 500 ... In-store network, 600 ... cloud, 700 ... data center, 700a ... database, T, T1-T5 ... store.

Claims (13)

It is equipped with a motion sensor that detects a person and a server that can communicate with this motion sensor.
The motion sensor is
It is equipped with an analysis unit that analyzes the data obtained by imaging a predetermined area and generates sensor data including the position of a person, attribute information, and staying time.
The server
An acquisition unit that acquires the sensor data from the motion sensor, and
A guidance system including a recommendation information generation unit that generates recommendation information that encourages the movement of the person based on the position, attribute information, and staying time of the person included in the acquired sensor data. The server further includes an identification unit that identifies the individual of the person based on the face image data of the person included in the acquired sensor data.
The guidance system according to claim 1, wherein the recommendation information generation unit generates recommendation information for promoting the movement of the identified individual based on the position and staying time of the person included in the acquired sensor data. .. The guidance system according to claim 2, wherein the identification unit associates the identified individual with a person corresponding to the individual and manages the identification unit in a database. The acquisition unit acquires congestion information of the predetermined area, and obtains the congestion information.
The guidance system according to any one of claims 1 to 3, wherein the recommendation information generation unit generates the recommendation information based on the congestion information. The acquisition unit acquires sale information in an area different from the predetermined area, and obtains the sale information.
The guidance system according to any one of claims 1 to 3, wherein the recommendation information generation unit generates the recommendation information based on the sale information. The acquisition unit acquires weather information of a geographical area including the predetermined area, and obtains weather information.
The guidance system according to any one of claims 1 to 3, wherein the recommendation information generation unit generates the recommendation information based on the acquired weather information. The guidance system according to any one of claims 1 to 6, further comprising a presentation unit that presents the recommendation information via an electronic advertising medium. A communication unit that communicates with a motion sensor that can generate sensor data including a person's position, attribute information, and staying time from image data obtained by imaging a predetermined area.
An acquisition unit that acquires the sensor data from the motion sensor, and
A server including a recommendation information generation unit that generates recommendation information for urging the movement of the person based on the position, attribute information, and staying time of the person included in the acquired sensor data. An identification unit for identifying the individual of the person is further provided based on the face image data of the person included in the acquired sensor data.
The server according to claim 8, wherein the recommendation information generation unit generates recommendation information for promoting the movement of the identified individual based on the position and staying time of the person included in the acquired sensor data. Instructions for operating the computer as a communication unit that communicates with a motion sensor capable of generating sensor data including a person's position, attribute information, and staying time from the data obtained by imaging a predetermined area.
Instructions for operating the computer as an acquisition unit for acquiring the sensor data from the motion sensor, and
Includes an instruction to operate the computer as a recommendation information generator that generates recommendation information that encourages the movement of the person based on the position, attribute information, and staying time of the person included in the acquired sensor data. ,program. An instruction for operating the computer as an identification unit for identifying the individual of the person based on the face image data of the person included in the acquired sensor data.
The tenth aspect of the present invention further includes an instruction for causing the computer to generate recommendation information for prompting the movement of the identified individual based on the position and staying time of the person included in the acquired sensor data. Program. It is a service providing method applied to a server capable of communicating with a motion sensor capable of generating sensor data including a person's position, attribute information and staying time from data obtained by imaging a predetermined area.
The process in which the server acquires the sensor data from the motion sensor, and
A service providing method including a process in which the server generates recommendation information for urging the movement of the person based on the position, attribute information, and staying time of the person included in the acquired sensor data. A process in which the server identifies an individual of the person based on the face image data of the person included in the acquired sensor data.
12. The process of claim 12, wherein the server further includes a process of generating recommendation information for promoting the movement of the identified individual based on the position and staying time of the person included in the acquired sensor data. Service provision method.

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