People flow measurement system, people flow measurement device, people flow measurement method, and people flow measurement program

Abstract

The moving body (200) includes a camera (210) and acquires video data (20) of a measurement area. The measuring unit (110) uses the video data (20) to measure the number of people in each of the multiple individual areas as an actual measurement result (21). The predicting unit (120) predicts the number of people in each of the multiple individual areas as a prediction result (22) using the actual measurement result (21) and a congestion prediction model (82). The analyzing unit (160) identifies an individual area with the lowest prediction accuracy among the multiple individual areas as a specific area based on the prediction result (22) and the actual measurement result (21), and estimates a person flow correlated with congestion in the specific area as a specific person flow. The route determining unit (170) determines a moving route (31) for the moving body (200) to acquire video data of the specific person flow.

Description

[Technical field]

[0001] The present disclosure relates to a people flow measurement system, a people flow measurement device, a people flow measurement method, and a people flow measurement program. [Background technology]

[0002] In recent years, congestion status display systems have been developed to grasp the actual degree of congestion in facilities where there is a lot of traffic, such as airports, train stations, shopping malls, theme parks, and event venues.

[0003] Patent Document 1 discloses a congestion status monitoring system that estimates the degree of congestion in an entire facility using a surveillance camera and visualizes the estimated degree of congestion. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2017 / 122258 Summary of the Invention [Problem to be solved by the invention]

[0005] In Patent Document 1, it is assumed that the surveillance cameras are installed so that they can capture the points that people always pass through when they move around, without any blind spots. Therefore, there is a problem that the cost of installing additional cameras is required in places where cameras are not installed.

[0006] An object of the present disclosure is to reduce the number of sensors and installation costs without reducing prediction accuracy by appropriately measuring the flow of people in a measurement area using a moving object. [Means for solving the problem]

[0007] The people flow measurement system according to the present disclosure comprises: a moving body that moves along a moving path in a measurement area having a plurality of individual areas, the moving body including a camera, and the moving body acquiring image data of the measurement area by the camera; a measurement unit that measures the number of people in each of the plurality of individual areas as an actual measurement result using the video data; a prediction unit that predicts the number of people in each of the plurality of individual areas as a prediction result using the actual measurement results and a congestion prediction model including a route model that is a model of a route of people flow in the measurement area and a structural model that represents a structure of the measurement area; an analysis unit that identifies an individual area having the lowest prediction accuracy among the plurality of individual areas as a specific area based on the prediction result and the actual measurement result, and estimates a pedestrian flow correlated with congestion in the specific area as a specific pedestrian flow; a route determination unit that determines the moving route for the moving body to acquire the image data of the specific people flow; Equipped with. Effect of the Invention

[0008] In the people flow measurement system according to the present disclosure, the people flow in a measurement area is measured using a moving object moving along an appropriate movement path. Therefore, the people flow measurement system according to the present disclosure has an effect of reducing the number of sensors and reducing installation costs without reducing prediction accuracy. [Brief description of the drawings]

[0009] [Figure 1] FIG. 2 is a diagram showing an example of a hardware configuration of the people flow measurement system according to the first embodiment. [Diagram 2] FIG. 2 is a diagram showing an example of a functional configuration of the people flow measurement system according to the first embodiment. [Diagram 3] FIG. 4 is a flow diagram showing a prediction process and a result determination correction process in the people flow measurement process according to the first embodiment. [Figure 4] FIG. 3 is a schematic diagram showing a prediction process according to the first embodiment. [Diagram 5]FIG. 4 is a schematic diagram showing a prediction result correction process of the result determination correction process according to the first embodiment. [Figure 6] FIG. 4 is a schematic diagram showing a parameter setting process in the result determination correction process according to the first embodiment. [Figure 7] FIG. 4 is a flow diagram showing a route determination process according to the first embodiment. [Figure 8] FIG. 4 is a schematic diagram showing a route determination process according to the first embodiment. [Figure 9] FIG. 13 is a diagram showing an example of the functional configuration of a people flow measuring device according to a modification of the first embodiment. [Figure 10] FIG. 13 is a diagram showing another example of the functional configuration of the people flow measurement device according to the modified example of the first embodiment. [Figure 11] FIG. 13 is a diagram showing another example of the hardware configuration of the people flow measuring device according to the modification of the first embodiment. [Figure 12] FIG. 11 is a diagram showing an example of a functional configuration of a people flow measurement system according to a second embodiment. [Figure 13] FIG. 11 is a flow diagram showing a route model generation process according to the second embodiment. [Figure 14] FIG. 11 is a schematic diagram showing a route model generation process according to the second embodiment. [Figure 15] FIG. 11 is a flow diagram showing a route model generation process according to the third embodiment. [Figure 16] FIG. 11 is a schematic diagram showing a route model generation process according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, the present embodiment will be described with reference to the drawings. In each drawing, the same or corresponding parts are given the same reference numerals. In the description of the embodiment, the description of the same or corresponding parts will be omitted or simplified as appropriate. The arrows in the drawing mainly indicate the flow of data or the flow of processing. In addition, the relationship of the size of each component in the following drawings may differ from the actual one. In addition, in the description of the embodiment, directions or positions such as up, down, left, right, front, back, front, and back may be indicated. These notations are for convenience of explanation and do not limit the arrangement, direction, or orientation of the device, instrument, or part.

[0011] Embodiment 1 ***Configuration Description*** FIG. 1 is a diagram showing an example of a hardware configuration of a people flow measurement system 500 according to this embodiment. The people flow measurement system 500 includes a people flow measurement device 100 and a moving object 200 . The moving body 200 includes a camera 210. The camera 210 captures an image of the surroundings and outputs image data 20. The moving body 200 moves along a movement path in a measurement area having a plurality of individual areas. In addition, the moving body 200 captures image data 20 of the measurement area by the camera 210 while moving along the movement path. Specifically, the moving body 200 is a moving object such as an AMR, a PMV, or a drone. AMR is an abbreviation for Autonomous Mobile Robot. PMV is an abbreviation for Personal Mobility Vehicle. Alternatively, the camera 210 may be attached to a person such as a security guard who moves along a moving route.

[0012] The people flow measuring device 100 is a computer. The people flow measuring device 100 includes a processor 910, as well as other hardware such as a memory 921, an auxiliary storage device 922, an input / output interface 930, and a communication device 950. The processor 910 is connected to the other hardware via signal lines and controls the other hardware.

[0013] The people flow measuring device 100 comprises, as functional elements, a measuring unit 110, a predicting unit 120, a result determining unit 130, a display correcting unit 140, a parameter setting unit 150, an analyzing unit 160, a path determining unit 170, and a memory unit 180. The measuring unit 110 comprises a video acquiring unit 111 and a people measuring unit 112. The functions of the measurement unit 110, the prediction unit 120, the result determination unit 130, the display correction unit 140, the parameter setting unit 150, the analysis unit 160, and the path determination unit 170 are realized by software. The storage unit 180 stores a result database 81 and a congestion prediction model 82. The congestion prediction model 82 includes a route model 821 and a structure model 822. The storage unit 180 is provided in a memory 921. The storage unit 180 may be provided in an auxiliary storage device 922, or may be provided in a distributed manner in the memory 921 and the auxiliary storage device 922.

[0014] In the following description, the functions of the measurement unit 110, the prediction unit 120, the result determination unit 130, the display correction unit 140, the parameter setting unit 150, the analysis unit 160, and the path determination unit 170 may be referred to as functions of the people flow measurement device 100. In addition, the measurement unit 110, the prediction unit 120, the result determination unit 130, the display correction unit 140, the parameter setting unit 150, the analysis unit 160, and the path determination unit 170 may be referred to as each unit of the people flow measurement device 100.

[0015] The processor 910 is a device that executes a people flow measurement program. The people flow measurement program is a program that realizes the functions of the people flow measurement device 100. The processor 910 is an IC that performs arithmetic processing. Specific examples of the processor 910 are a CPU, a DSP, and a GPU. IC is an abbreviation for Integrated Circuit. CPU is an abbreviation for Central Processing Unit. DSP is an abbreviation for Digital Signal Processor. GPU is an abbreviation for Graphics Processing Unit.

[0016] The memory 921 is a storage device that temporarily stores data. Specific examples of the memory 921 are SRAM and DRAM. SRAM is an abbreviation for Static Random Access Memory. DRAM is an abbreviation for Dynamic Random Access Memory. The auxiliary storage device 922 is a storage device that stores data. A specific example of the auxiliary storage device 922 is a HDD. The auxiliary storage device 922 may also be a portable storage medium such as an SD (registered trademark) memory card, CF, NAND flash, a flexible disk, an optical disk, a compact disk, a Blu-ray (registered trademark) disk, or a DVD. Note that HDD is an abbreviation for Hard Disk Drive. SD (registered trademark) is an abbreviation for Secure Digital. CF is an abbreviation for CompactFlash (registered trademark). DVD is an abbreviation for Digital Versatile Disk.

[0017] The input / output interface 930 is an interface for connecting an input / output device. Specific examples of the input / output interface 930 include a USB and an HDMI (registered trademark) port. USB is an abbreviation for Universal Serial Bus. HDMI (registered trademark) is an abbreviation for High-Definition Multimedia Interface. Examples of input devices include a mouse, a keyboard, a touch panel, etc. Examples of output devices include a display device such as a monitor, and a printer, etc.

[0018] The communication device 950 has a receiver and a transmitter, and performs wired or wireless communication. The communication device 950 is connected to a communication network such as a LAN, the Internet, a telephone line, or Wi-Fi (registered trademark). Specifically, the communication device 950 is a communication chip or a NIC. NIC is an abbreviation for Network Interface Card.

[0019] The people flow measurement program is executed in the people flow measurement device 100. The people flow measurement program is read into the processor 910 and executed by the processor 910. In addition to the people flow measurement program, an OS is also stored in the memory 921. The OS is an abbreviation for Operating System. The processor 910 executes the people flow measurement program while executing the OS. The people flow measurement program and the OS may be stored in an auxiliary storage device 922. The people flow measurement program and the OS stored in the auxiliary storage device 922 are loaded into the memory 921 and executed by the processor 910. Note that a part or all of the people flow measurement program may be incorporated into the OS.

[0020] The people flow measurement device 100 may include multiple processors that replace the processor 910. These multiple processors share the task of executing the people flow measurement program. Each processor is a device that executes the people flow measurement program, just like the processor 910.

[0021] Data, information, signal values ​​and variable values ​​used, processed or output by the people flow measurement program are stored in memory 921, secondary storage device 922, or in registers or cache memory within processor 910.

[0022] The "part" of each part of the people flow measuring device 100 may be read as a "circuit," "step," "procedure," "process," or "circuitry." The people flow measuring program causes a computer to execute each process of the people flow measuring device 100, where the "part" of each part is read as a "process." That is, the people flow measuring program causes a computer to execute a measurement process, a prediction process, a result determination process, a display correction process, a parameter setting process, an analysis process, and a route determination process. The "process" of each process of the people flow measuring device 100 may be read as a "program," a "program product," a "computer-readable storage medium storing a program," or a "computer-readable recording medium recording a program." The people flow measuring method is a method performed by the people flow measuring device 100 executing the people flow measuring program. The people flow measurement program may be provided by being stored in a computer-readable recording medium. Also, the people flow measurement program may be provided as a program product.

[0023] ***Feature Description*** FIG. 2 is a diagram illustrating an example of a functional configuration of a people flow measurement system 500 according to this embodiment. The people flow measurement system 500 includes a people flow measurement device 100 and a moving object 200 . The moving body 200 includes a camera 210. The moving body 200 moves along a movement path 31 in a measurement area having a plurality of individual areas. The moving body 200 also captures video data 20 of the measurement area by the camera 210 while moving along the movement path. The people flow measuring device 100 acquires video data 20 from a moving object 200 via a communication device 950 .

[0024] The people flow measurement system 500 has a prediction function by the measurement unit 110 and the prediction unit 120, and a route determination function by the analysis unit 160 and the route determination unit 170 that determines the route of the moving object 200. Furthermore, the people flow measurement system 500 has a result determination correction function by the result determination unit 130, the display correction unit 140, and the parameter setting unit 150. The result determination correction function is a function that displays congestion more appropriately and optimizes the parameters of the route model 821 so that the route model 821 reflects actual measured values.

[0025] <Prediction function> The image acquisition unit 111 of the measurement unit 110 acquires the image data 20 . The number of people counting section 112 of the measurement section 110 uses the video data 20 to count the number of people in each of the multiple individual areas as an actual measurement result 21.

[0026] The prediction unit 120 predicts the number of people in each of the multiple individual areas as a prediction result 22, using the actual measurement result 21 and the congestion prediction model 82. The congestion prediction model 82 includes a route model 821 and a structure model 822. The route model 821 is a model of the route of people flow in the measurement area. The route model 821 is a model that represents the routes that people take in the measurement area and branch off from one another. The structure model 822 represents the structure of the measurement area. The structure model 822 is a model that represents the structure of the measurement area, such as map information, structures, facility information, and area of ​​the measurement area.

[0027] Specifically, the prediction unit 120 uses the actual measurement result 21 and the congestion prediction model 82 to calculate the prediction result 22 by a multi-agent simulator. The multi-agent simulator is also called MAS. The result database 81 stores the actual measurement results 21 and the predicted results 22 .

[0028] <Route determination function> Based on the prediction results 22 and the actual measurement results 21, the analysis unit 160 identifies the individual area with the lowest prediction accuracy among the multiple individual areas as a specific area, and estimates a pedestrian flow correlated with congestion in the specific area as the specific pedestrian flow. Then, the route determination unit 170 determines a movement route 31 for the moving object 200 to acquire video data of the specific people flow.

[0029] <Result judgment correction function> The result determination unit 130 determines whether the difference between the predicted result 22 and the actual measurement result 21 is equal to or greater than a threshold. The threshold is assumed to be appropriately set in advance and stored in the storage unit 180. If the difference between the prediction result 22 and the actual measurement result 21 is equal to or greater than a threshold, the display correction unit 140 corrects the prediction result 22 to the actual measurement result 21. Then, the display correction unit 140 displays the congestion status of the measurement area on the display device based on the prediction result 22. Specifically, the display correction unit 140 displays the congestion status of each of the multiple individual areas on the display device. The display device is also called a congestion degree display device that displays the congestion status of the measurement area.

[0030] Furthermore, if the difference between the prediction result 22 and the actual measurement result 21 is equal to or greater than a threshold, the parameter setting unit 150 sets or updates the parameters of the route model 821 so that the route model 821 reflects the contents of the actual measurement result 21. The threshold value used to determine whether or not to correct the predicted result 22 to the actual measurement result 21 and the threshold value used to determine whether or not to reflect the contents of the actual measurement result 21 in the route model 821 may be different values. For example, the threshold used to determine whether to reflect the contents of the actual measurement result 21 in the route model 821 may be smaller than the threshold used to determine whether to correct the predicted result 22 to the actual measurement result 21, or vice versa.

[0031] ***Explanation of Operation*** Next, an operation of the people flow measurement system 500 according to this embodiment will be described. The operation procedure of the people flow measurement system 500 corresponds to a people flow measurement method. Moreover, a program that realizes the operation of the people flow measurement system 500 corresponds to a people flow measurement program that executes a people flow measurement process.

[0032] The people flow measurement process, which is the operation of the people flow measurement system 500, includes a prediction process by the measurement unit 110 and the prediction unit 120, and a route determination process by the analysis unit 160 and the route determination unit 170 to determine the route of the moving object 200. Furthermore, the people flow measurement process includes a result determination correction process by the result determination unit 130, the display correction unit 140, and the parameter setting unit 150. The result determination correction process is a process that displays congestion more appropriately and optimizes the parameters of the route model 821 so that the route model 821 reflects actual measured values.

[0033] FIG. 3 is a flow diagram showing a prediction process and a result determination correction process in the people flow measurement process according to the present embodiment.

[0034] <Prediction processing> In the prediction process, the video acquisition unit 111 acquires the video data 20 output from the moving object 200 via the communication device 950. Specifically, each of the multiple moving objects 200 captures an image of the measurement area while moving along a movement path 31 in the measurement area, and transmits the video data 20 to the people flow measurement device 100. The video acquisition unit 111 receives the video data 20 from each of the multiple moving objects 200.

[0035] In step S101, the people counting unit 112 counts the number of people appearing in the video data 20. Specifically, the people counting unit 112 measures an actual measurement result in Tn using the video data 20 transmitted from multiple moving objects 200, where n is a natural number. The actual measurement result in Tn is the number of people in each of multiple individual areas included in the measurement area in Tn.

[0036] In step S102, the prediction unit 120 predicts congestion in Tn+1 using the actual measurement results in Tn as input values. Specifically, the prediction unit 120 predicts the number of people in each individual area in Tn+1 as a prediction result using the actual measurement results in Tn and the congestion prediction model 82. More specifically, the prediction unit 120 predicts the number of people in each individual area in Tn+1 as a prediction result using the actual measurement results 21 in Tn and the congestion prediction model 82 with a multi-agent simulator.

[0037] When time has passed from Tn to Tn+1 (step S103), the people counting unit 112 uses the video data 20 to count the number of people in each individual area at Tn+1 as an actual measurement result.

[0038] <Result determination correction process: Steps S105 to S111> In step S105, the result determination unit 130 compares the actual measurement result at Tn+1 with the predicted result at Tn+1 to determine whether there is a difference. Specifically, it determines whether the difference between the predicted result at Tn+1 and the actual measurement result at Tn+1 is equal to or greater than a threshold. If the difference between the predicted result at Tn+1 and the actual measurement result at Tn+1 is equal to or greater than the threshold, the process proceeds to step S106. If the difference between the predicted result at Tn+1 and the actual measurement result at Tn+1 is smaller than the threshold value, the process proceeds to step S108.

[0039] If it is determined that there is a difference between the actual measurement result at Tn+1 and the predicted result at Tn+1, in step S106, the display correction unit 140 replaces the predicted value with the measured value. Specifically, if the difference between the actual measurement result at Tn+1 and the predicted result at Tn+1 is equal to or greater than a threshold, the display correction unit 140 replaces the predicted result at Tn+1 with the actual measurement result at Tn+1 to correct the predicted result at Tn+1.

[0040] If it is determined that there is a difference between the actual measurement result at Tn+1 and the predicted result at Tn+1, in step S107, the parameter setting unit 150 corrects the parameters near the individual area determined to have a difference, and reflects the actual measurement result in the congestion prediction model 82. Specifically, if the difference between the actual measurement result at Tn+1 and the predicted result at Tn+1 is equal to or greater than a threshold, the parameter setting unit 150 determines the parameters of the route model 821 so that the route model 821 reflects the contents of the actual measurement result. The parameters of the route model 821 are, for example, elements such as the history to the destination and the interactions between agents.

[0041] As described above, the threshold value used to determine whether to correct the predicted result at Tn+1 to the actual measurement result at Tn+1 and the threshold value used to determine whether to reflect the contents of the actual measurement result at Tn+1 in the route model 821 may be different values.

[0042] In step S108, n=n+1 is set, and the process proceeds to step S109.

[0043] In step S109, the display correction unit 140 displays the congestion status in each of the multiple individual areas based on the prediction result at Tn+1. The display correction unit 140 may display the congestion status in each of the multiple individual areas on a display device connected to the people flow measurement device 100 via the output interface. Alternatively, the display correction unit 140 may transmit the prediction result to another device via the communication device 950. The other device is a device that visualizes the congestion degree of the measurement area based on the prediction result.

[0044] In step S110, it is determined whether the congestion display time has ended. If the congestion display time has not ended, the process proceeds to step S111. If the congestion display time has ended, the process ends.

[0045] In step S111, the prediction unit 120 predicts congestion in Tn+1 using the actual measurement result in Tn as an input value, and obtains a prediction result for Tn+1. Then, the process returns to step S103 and the process is repeated. The prediction method in step S111 is the same as the prediction method in step S102.

[0046] FIG. 4 is a schematic diagram showing the prediction process according to the present embodiment. FIG. 5 is a schematic diagram showing the display correction process of the result determination correction process according to the present embodiment. FIG. 6 is a schematic diagram showing the parameter setting process of the result determination correction process according to the present embodiment. A specific example of people flow measurement processing will be described with reference to FIG. 4 to FIG. 4 to 6, the black circles represent the actual measurement results of the number of people, and the white circles represent the predicted results of the number of people. The actual measurement results are also called actual values. The predicted results are also called forecast values.

[0047] As shown in (1) of Fig. 4, a moving object 200 acquires video data 20, which is people flow data, while moving in a measurement area 300. In Fig. 4, the measurement area 300 is composed of multiple individual areas. Specifically, the individual areas refer to areas such as areas A, B, C, D, E, and F, and routes connecting the areas. Alternatively, the individual areas may refer to areas A, B, C, D, E, and F.

[0048] As shown in Figure 4 (2), a people flow simulation using MAS is performed in Tn to predict the people flow and congestion in the measurement area in Tn+1. In Figure 4 (2), the number of people in areas A, B, C, D, E, and F, as well as the routes connecting each area, are obtained as the predicted results in Tn+1.

[0049] As shown in (3) of Fig. 5, the predicted result at Tn+1 is compared with the actual measurement result at Tn+1, and if there is a difference, the result is corrected. Then, the corrected result is displayed. As shown in the left diagram of Fig. 5, the actual measurement result for area D in Tn+1 is 6 people. Meanwhile, as shown in the right diagram of Fig. 4, the predicted result for area D in Tn+1 is 1 person. The threshold is assumed to be 4 people. In this case, the result determination unit 130 determines whether the difference between the predicted result of 1 person in Tn+1 and the actual measurement result of 6 people in Tn+1 is equal to or greater than the threshold of 4 people. Since the difference between the predicted result and the actual measurement result is equal to or greater than the threshold, the display correction unit 140 corrects the predicted result of 1 person at Tn+1 to the actual measurement result of 6 people at Tn+1.

[0050] In addition, since the difference between the predicted result and the actual measurement result is equal to or greater than the threshold, the parameter setting unit 150 modifies the parameters of the route model in the simulation executed at Tn+1 based on the actual measurement result at Tn+1. Specifically, as shown in FIG. 6, the parameters are modified as follows. For example, since the predicted value of area D was significantly different from the actual measured value, the migration branching rate, which is one of the MAS parameters, is modified as follows: Increase the rate of people moving between ADs. Reduce the migration branching rate between DE and D. The parameters are changed in this way and used in the next simulation.

[0051] In addition, a data assimilation method may be used as a method for automatically adjusting parameters. The data assimilation method is a method for incorporating measured data into a numerical simulation. This method determines values ​​that reduce the difference between the simulation output and the observed data, and determines more plausible parameters.

[0052] FIG. 7 is a flow diagram showing the route determination process according to the present embodiment. The route determination process is a process for appropriately determining the movement route of the moving object 200. The route determination process is performed after the prediction process has been performed and a sufficient amount of prediction results and actual measurement results have been accumulated in the result database 81. Therefore, the route determination process is a process that is performed asynchronously with the result determination and correction process.

[0053] <Route determination process> In step S201, based on the prediction results and actual measurement results stored in the result database 81, the analysis unit 160 identifies, among a plurality of individual regions, an individual region having the lowest prediction accuracy as a specific region. In step S202, the analysis unit 160 estimates a pedestrian flow that is correlated with congestion in a specific area as a specific pedestrian flow. Specifically, the analysis unit 160 performs a correlation analysis to identify a location that is influencing the congestion in the identified specific area. In step S203, the analysis unit 160 estimates the people flow measurement point having the highest correlation with congestion in the specific area as the specific people flow.

[0054] In step S204, the route determination unit 170 determines a moving route 31 suitable for the moving object 200 to acquire video data of a specific people flow. Specifically, the route determination unit 170 determines the moving route 31 so as to focus on passing through the location of the specific people flow. In step S205, the route determination unit 170 transmits the travel route 31 to each moving object.

[0055] FIG. 8 is a schematic diagram showing the route determination process according to the present embodiment. Specifically, the movement route 31 of the moving object 200 is optimized in the following manner. In (11) of FIG. 8, the analysis unit 160 identifies an area E where the difference between the actual measurement result and the predicted result is large as a specific region. 8 (12), the analysis unit 160 estimates the pedestrian flow that affects the congestion in area E from the actual measurement results and prediction results acquired up to now, using a technique such as correlation analysis. From the data acquired up to now, a correlation analysis with the congestion in area E is performed, and it is estimated which pedestrian flow has the greatest impact. In (12) of FIG. 8, the route determination unit 170 sets the movement route 31 so as to focus on acquiring estimated people flow data.

[0056] ***Other configurations*** <Variation 1> In this embodiment, the people flow measuring device 100 has a configuration capable of executing a prediction process, a result determination and correction process, and a route determination process. On the other hand, the people flow measuring device 100 may be configured to perform only the prediction process and the result determination and correction process. Also, the people flow measuring device 100 may be configured to perform only the prediction process and the route determination process.

[0057] FIG. 9 is a diagram showing an example of a functional configuration of the people flow measuring device 100 according to a modified example of the present embodiment. 9 performs only the prediction process and the result determination and correction process, that is, the analysis unit 160, the route determination unit 170, and the result database 81 are not necessary.

[0058] FIG. 10 is a diagram showing another example of the functional configuration of the people flow measuring device 100 according to the modified example of this embodiment. 10 performs only the prediction process and the route determination process, that is, the result determination unit 130, the display correction unit 140, and the parameter setting unit 150 are not necessary. In the people flow measuring device 100 shown in FIG. 10, the prediction result 22 output by the prediction unit 120 is output to the congestion degree display device.

[0059] <Variation 2> In this embodiment, the functions of each unit of the people flow measuring device 100 are realized by software. As a variation, the functions of each unit of the people flow measuring device 100 may be realized by hardware. Specifically, the people flow measuring device 100 includes an electronic circuit 909 instead of a processor 910 .

[0060] FIG. 11 is a diagram showing another example of the hardware configuration of the people flow measuring device 100 according to a modified example of this embodiment. The electronic circuit 909 is a dedicated electronic circuit that realizes the functions of each part of the people flow measurement device 100. Specifically, the electronic circuit 909 is a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, a logic IC, a GA, an ASIC, or an FPGA. GA is an abbreviation for Gate Array. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field-Programmable Gate Array.

[0061] The functions of each part of the people flow measuring device 100 may be realized by one electronic circuit, or may be distributed across multiple electronic circuits.

[0062] As another modification, some of the functions of each unit of the people flow measuring device 100 may be realized by electronic circuits, and the remaining functions may be realized by software. Also, some or all of the functions of each unit of the people flow measuring device 100 may be realized by firmware.

[0063] Each of the processor and the electronic circuit is also called a processing circuitry. In other words, the functions of each part of the people flow measurement device 100 are realized by the processing circuitry.

[0064] ***Explanation of the Effects of the Present Embodiment*** In simulating people flow using MAS, a route model is set up to determine the routes people will take. For this reason, it is necessary to either know from experience the behavior of people in the facility where the people flow is to be measured, or to install sensors in many locations and observe them in advance. In the people flow measurement system according to the present embodiment, the actual measurement results can be appropriately reflected in the route model, which has the effect of enabling the route model to be created accurately and at low cost. The people flow measurement system according to the present embodiment uses moving objects for measurement, but the system has the advantage of being able to display congestion in the entire measurement area by interpolating areas where no sensors are present in the simulation results.

[0065] In addition, in a typical congestion display system, it is assumed that the cameras are installed in a place where they can capture the points that people pass through when moving from one area to another without any blind spots, so it is costly to install additional cameras in places where cameras are not installed. On the other hand, the people flow measurement system according to the present embodiment measures using moving objects, which has the effect of reducing the number of sensors and reducing installation costs. Also, the people flow measurement system according to the present embodiment can focus on measuring data that is the cause of locations with low prediction accuracy, thereby improving the accuracy of the congestion degree.

[0066] Embodiment 2 In this embodiment, differences from and additions to the first embodiment will be mainly described. In this embodiment, components having the same functions as those in the first embodiment are given the same reference numerals, and the description thereof will be omitted. In this embodiment, a mode in which a route model is automatically generated will be described.

[0067] ***Configuration Description*** FIG. 12 is a diagram illustrating an example of a functional configuration of a people flow measurement system 500 according to this embodiment. The people flow measurement device 100 according to this embodiment includes a movement trajectory accumulation unit 190, a movement trajectory database 101, and a model generation unit 102 in addition to the configuration of Fig. 9 described in embodiment 1. Here, new functional elements described in this embodiment are added to the configuration of Fig. 9 described in embodiment 1. Note that new functional elements described in this embodiment may be added to the configuration of Fig. 2 or Fig. 10 described in embodiment 1.

[0068] The movement trajectory accumulation unit 190 accumulates the trajectory of a person moving in the measurement area as movement trajectory information in the movement trajectory database 101 based on the video data 20. The movement trajectory accumulation unit 190 includes a person detection unit 191, a person identification unit 192, and a movement trajectory calculation unit 193. The model generation unit 102 generates a route model 821 based on the movement trajectory information stored in the movement trajectory database 101 .

[0069] ***Explanation of Operation*** In this embodiment, a route model generation process will be described in which a route model 821 is generated based on video data 20 acquired from a moving object 200 by a movement trajectory storage unit 190, a movement trajectory database 101, and a model generation unit 102.

[0070] FIG. 13 is a flow diagram showing the route model generation process according to the present embodiment. In the route model generation process, a moving object 200 is placed at the entrance of a measurement area, acquires video data 20 of the entrance, and transmits it to the people flow measurement device 100 .

[0071] In step S301, the human detection unit 191 detects a human who has entered a measurement area as a person to be tracked, based on the video data 20 acquired from the moving object 200. In step S302, the person identification unit 192 identifies a person to be tracked based on the video data 20 acquired from the moving object 200, and tracks the person to be tracked until the person exits the measurement area. In step S303, the movement trajectory calculation unit 193 registers the movement trajectory of the person to be tracked in the movement trajectory database 101. In step S304, the movement trajectory calculation unit 193 moves the moving object 200 to the entrance.

[0072] In step S305, the movement trajectory calculation unit 193 determines whether sufficient data for creating the route model 821 has been acquired. If it is determined that the amount is not sufficient to generate the route model 821, the process returns to step S301 and the process is repeated. If it is determined that sufficient data has been acquired to create the route model 821, the process proceeds to step S306.

[0073] In step S306, the model generation unit 102 tally up the trajectories stored in the trajectory database 101. In step S307, the model generation unit 102 generates a route model 821 based on the result of tallying up the movement trajectories. In step S308, the model generation unit 102 reflects the route model 821 in the congestion prediction model 82.

[0074] FIG. 14 is a schematic diagram showing the route model generation process according to the present embodiment. Specifically, the route model is generated in the following procedure. 14 (21), a person who enters a measurement area is tracked by the moving object 200 until the person exits, and a movement trajectory is acquired. When a person is detected at the entrance, the moving object 200 may track the person to be tracked until the person exits. Alternatively, the person identification unit 192 may analyze video data from multiple moving objects 200 to track the person to be tracked from the time of entry to the time of exit.

[0075] 14(21) is repeated, and when sufficient movement trajectory information has been acquired, the model generation unit 102 creates a route model from the movement trajectory, as shown in Fig. 14(22). Then, the route model 821 is reflected in the congestion prediction model 82. After that, the MAS is executed as described in the first embodiment.

[0076] ***Explanation of the Effects of the Present Embodiment*** As described above, the people flow measurement device according to the present embodiment can automatically create a route model. In addition, by reflecting information on tracking people in the route model, it is possible to perform a simulation that is closer to the real environment, thereby improving the estimation accuracy.

[0077] Embodiment 3 In this embodiment, differences from the second embodiment and additional features to the second embodiment will be mainly described. In this embodiment, components having the same functions as those in the second embodiment are given the same reference numerals, and the description thereof will be omitted.

[0078] ***Configuration Description*** The configuration of the people flow measuring device 100 according to this embodiment is similar to that of the second embodiment. The movement trajectory accumulation unit 190 accumulates the trajectory for each attribute of a person moving in the measurement area in the movement trajectory database 101 as movement trajectory information. A model generation unit 102 generates a route model based on the movement trajectory information stored in the movement trajectory database 101, taking into account the attributes of a person moving through a measurement area.

[0079] ***Explanation of Operation*** FIG. 15 is a flow diagram showing the route model generation process according to the present embodiment. In the route model generation process, similarly to the second embodiment, it is assumed that the moving object 200 is placed at the entrance of the measurement area and outputs the video data 20 of the entrance.

[0080] In step S401, the person detection unit 191 detects a person who has entered the measurement area as a person to be tracked, based on the video data 20 acquired from the moving object 200. At this time, the person detection unit 191 also detects attributes of the person to be tracked. For example, the person detection unit 191 detects attributes such as whether the person to be tracked is male or female. In step S402, the person identification unit 192 identifies a person to be tracked based on the video data 20 acquired from the moving object 200, and tracks the person to be tracked until the person exits the measurement area. In step S403, the movement trajectory calculation unit 193 accumulates the movement trajectory of the person to be tracked in the movement trajectory database 101 together with the attributes of the person to be tracked. In step S404, the movement trajectory calculation unit 193 moves the moving object 200 to the entrance.

[0081] In step S405, the movement trajectory calculation unit 193 determines whether sufficient data for creating the route model 821 has been acquired. If it is determined that the amount is not sufficient to create the route model 821, the process returns to step S401 and the process is repeated. If it is determined that sufficient data has been acquired to create the route model 821, the process proceeds to step S406.

[0082] In step S406, the model generation unit 102 counts the trajectories stored in the trajectory database 101 for each attribute. In step S407, the model generation unit 102 generates a route model 821 that takes into account the attributes, based on the results of tallying the trajectories for each attribute. In step S408, the model generation unit 102 reflects the route model 821 in the congestion prediction model 82.

[0083] FIG. 16 is a schematic diagram showing the route model generation process according to the present embodiment. Specifically, a route model that takes into account attributes is generated in the following procedure. In (31) of FIG. 16, when detecting a person who has entered the measurement area, the attributes of the person are also detected. For example, it is detected whether the person to be tracked is male or female. Then, the moving body 200 tracks the person until he / she exits, and a movement trajectory for each attribute is obtained. When a person is detected at the entrance, the moving body 200 may track the person to be tracked until he / she exits. Alternatively, the person identification unit 192 may analyze video data from multiple moving bodies 200 to track the person to be tracked from entry to exit.

[0084] After repeating (31) in Fig. 16 and acquiring sufficient trajectory information, the model generation unit 102 creates a route model according to the attributes from the acquired attributes and trajectories, as shown in (32) in Fig. 16. Then, the route model 821 is reflected in the congestion prediction model 82. After that, the MAS is executed as described in the first embodiment. In FIG. 16, the attribute of male or female is detected, but other attributes such as age, number of people in a group, and whether or not the person is a family may also be detected.

[0085] ***Explanation of the Effects of the Present Embodiment*** As described above, the people flow measurement device according to the present embodiment can automatically create a route model according to attributes. In addition, the congestion level can be estimated by the route model according to attributes using information obtained by tracking people whose attributes have been detected, thereby improving the estimation accuracy.

[0086] In the above first to third embodiments, each part of the people flow measuring device has been described as an independent functional block. However, the configuration of the people flow measuring device does not have to be as in the above-mentioned embodiments. The functional blocks of the people flow measuring device may have any configuration as long as they can realize the functions described in the above-mentioned embodiments. In addition, the people flow measuring device may not be a single device, but may be a system composed of multiple devices. In addition, it is possible to combine a plurality of parts of the first to third embodiments. Alternatively, it is possible to implement only one part of these embodiments. In addition, it is possible to implement any combination of these embodiments, either as a whole or in part. That is, in the first to third embodiments, the embodiments can be freely combined, any of the components in each embodiment can be modified, or any of the components in each embodiment can be omitted.

[0087] The above-described embodiment is essentially a preferred example, and is not intended to limit the scope of the present disclosure, the scope of application of the present disclosure, and the scope of use of the present disclosure. The above-described embodiment can be modified in various ways as necessary. For example, the procedure described using a flow chart or sequence chart may be modified as appropriate. [Explanation of symbols]

[0088] 20 video data, 21 actual measurement results, 22 predicted results, 31 movement route, 81 result database, 82 congestion prediction model, 821 route model, 822 structural model, 100 people flow measurement device, 101 movement trajectory database, 102 model generation unit, 110 measurement unit, 111 image acquisition unit, 112 people counting unit, 120 prediction unit, 130 result determination unit, 140 display correction unit, 150 parameter setting unit, 160 analysis unit, 170 route determination unit, 180 memory unit, 190 movement trajectory accumulation unit, 191 person detection unit, 192 person identification unit, 193 movement trajectory calculation unit, 200 moving object, 210 camera, 300 measurement area, 500 people flow measurement system, 909 electronic circuit, 910 processor, 921 memory, 922 auxiliary storage device, 930 Input / Output Interface, 950 Communications Device.

Claims

1. A moving body that moves along a moving path in a measurement area having a plurality of individual areas, equipped with a camera, and acquires video data of the measurement area with the camera; A measurement unit that measures, as an actual measurement result, the number of people in each of the plurality of individual areas using the video data; A prediction unit that predicts, as a prediction result, the number of people in each of the plurality of individual areas using the actual measurement result, a path model that is a model of the flow path of the flow of people in the measurement area, and a congestion prediction model including a structure model representing the structure of the measurement area; An analysis unit that identifies, as a specific area, the individual area with the lowest prediction accuracy among the plurality of individual areas based on the prediction result and the actual measurement result, and estimates the flow of people correlated with the congestion in the specific area as a specific flow of people; A path determination unit that determines the moving path for the moving body to acquire video data of the specific flow of people A people flow measurement system comprising the same.

2. The people flow measurement system further comprises: A result determination unit that determines whether the difference between the prediction result and the actual measurement result is equal to or greater than a threshold value; A display correction unit that corrects the prediction result to the actual measurement result if the difference is equal to or greater than the threshold value, and displays the congestion status in each of the plurality of individual areas based on the prediction result. The people flow measurement system according to Claim 1, comprising the same.

3. The people flow measurement system further comprises: A parameter setting unit that determines the parameters of the path model so that the path model reflects the content of the actual measurement result if the difference is equal to or greater than the threshold value. The people flow measurement system according to Claim 2, comprising the same.

4. The people flow measurement system further comprises: A moving trajectory accumulation unit that accumulates, as moving trajectory information, the trajectories of people moving in the measurement area in a moving trajectory database based on the video data; A model generation unit that generates the path model based on the moving trajectory information accumulated in the moving trajectory database. The people flow measurement system according to any one of Claims 1 to 3, comprising the same.

5. The moving trajectory accumulation unit: Accumulates, in the moving trajectory database, the trajectories of people moving in the measurement area for each attribute of the people as the moving trajectory information; The model generation unit: Generates the path model taking into account the attributes of the people moving in the measurement area based on the moving trajectory information accumulated in the moving trajectory database. The people flow measurement system according to Claim 4, comprising the same.

6. The prediction unit: The crowd flow measurement system according to any one of claims 1 to 3, wherein the prediction result is calculated by a multi-agent simulator using the measured result and the congestion prediction model.

7. A moving body that moves along a moving path in a measurement area having a plurality of individual areas, equipped with a camera, acquires the video data from the moving body that acquires the video data of the measurement area by the camera, and uses the video data to measure the number of people in each of the plurality of individual areas as a measured result, a measurement unit; A prediction unit that predicts the number of people in each of the plurality of individual areas as a prediction result using the measured result, a congestion prediction model including a path model that is a model of the path of the crowd flow in the measurement area and a structure model that represents the structure of the measurement area; An analysis unit that identifies, as a specific area, the individual area with the lowest prediction accuracy among the plurality of individual areas based on the prediction result and the measured result, and estimates the crowd flow correlated with the congestion in the specific area as a specific crowd flow; A path determination unit that determines the moving path for the moving body to acquire the video data of the specific crowd flow A crowd flow measurement device comprising.

8. A computer is a moving body that moves along a moving path in a measurement area having a plurality of individual areas, equipped with a camera, acquires the video data from the moving body that acquires the video data of the measurement area by the camera, and uses the video data to measure the number of people in each of the plurality of individual areas as a measured result, The computer predicts the number of people in each of the plurality of individual areas as a prediction result using the measured result, a congestion prediction model including a path model that is a model of the path of the crowd flow in the measurement area and a structure model that represents the structure of the measurement area, The computer identifies, as a specific area, the individual area with the lowest prediction accuracy among the plurality of individual areas based on the prediction result and the measured result, and estimates the crowd flow correlated with the congestion in the specific area as a specific crowd flow, A crowd flow measurement method in which the computer determines the moving path for the moving body to acquire the video data of the specific crowd flow.

9. A moving body that moves along a moving path in a measurement area having a plurality of individual areas, equipped with a camera, obtains the video data from the moving body that obtains the video data of the measurement area by the camera, and measures the number of people in each of the plurality of individual areas as an actual measurement result using the video data, and a prediction process of predicting the number of people in each of the plurality of individual areas as a prediction result using the actual measurement result, a congestion prediction model including a path model that is a model of the path of the flow of people in the measurement area, and a structure model that represents the structure of the measurement area, and an analysis process of identifying, as a specific area, the individual area with the lowest prediction accuracy among the plurality of individual areas based on the prediction result and the actual measurement result, and estimating the flow of people correlated with the congestion in the specific area as a specific flow of people, and a path determination process of determining the moving path for the moving body to obtain the video data of the specific flow of people A people flow measurement program for causing a computer to execute.

Images