Information processing device and information processing method

Abstract

This information processing device comprises: an area setting unit for setting at least one area in a moving image representing a target space, which is a space to be analyzed; a detection unit for detecting, from the moving image, a specific object positioned in the at least one area in a target time slot, which is a time slot to be analyzed, and tracking the detected object; and a drawing unit for drawing an image by superimposing each position of the object detected by the detection unit on the image of the target space.

Description

Information processing device and information processing method

[0001] The present invention relates to an information processing device and an information processing method.

[0002] There is known a technique for acquiring the flow of people and the duration of their stay in a specific area by analyzing an image of the specific area. For example, Patent Literature 1 describes a technique for counting the number and flow of people in a store based on a video of the store interior captured by a camera installed on the ceiling or the like of the store.

[0003] Japanese Patent Application Laid-Open No. 2006-285409

[0004] As one way of understanding people flow, there is a demand for intuitively visualizing the flow of objects passing through a specific route in a space to be analyzed.

[0005] Therefore, an object of the present disclosure is to make it possible to easily recognize the flow of objects along a specific route in a space.

[0006] In order to solve the above problem, an information processing device according to one aspect of the present disclosure includes an area setting unit that sets at least one area in a moving image that represents a target space, which is the space to be analyzed; a detection unit that detects specific objects located within the at least one area from the moving image during a target time period, which is the time period to be analyzed, and tracks the detected objects; and a drawing unit that draws each position of the objects detected by the detection unit by superimposing it on an image of the target space.

[0007] According to the above aspect, an area is set in a target space, and based on tracking of an object located within the area, each position of the object over time is drawn superimposed on an image of the target space. This visualizes the movement of the object through the area, making it easy to recognize at least the flow of objects whose specific route is to pass through the area.

[0008] It becomes possible to easily recognize the flow of objects along a specific route in a space.

[0009] 1 is a block diagram showing the functional configuration of an information processing device of this embodiment. FIG. 1 is a diagram showing an example of two pairs of analysis target areas set based on an image of a target space to be analyzed and a specified input. FIG. 2 is a diagram showing an example of an image of a target space to be analyzed and four analysis target areas each set based on a specified input. FIG. 3 is a diagram showing an example of a figure drawn to indicate the position of a person, the figure being a triangle showing the first and second positions in time series of a moving person. FIG. 4 is a diagram showing an example of an image of the target space in which the position of a person moving from a first area to a second area of ​​a pair of analysis target areas is drawn. FIG. 5 is a diagram showing an example of an image of the target space in which the position of a person who has flowed into the analysis target area is drawn. FIG. 6 is a diagram showing an example of an image of the target space in which the position of a person who has flowed out of the analysis target area is drawn. FIG. 7 is a diagram showing an example of an image of the target space in which the position of a person who has been detected for the first time in the analysis target area and flowed out of the analysis target area is drawn. FIG. 8 is a diagram showing an example of an image of the target space in which the position of a person who has stayed in the analysis target area is drawn.

[0010] An information processing device according to an embodiment of the present invention will be described with reference to the drawings. Whenever possible, the same components are designated by the same reference numerals, and redundant description will be omitted.

[0011] 1 is a block diagram showing the device configuration of an information processing system including an information processing device according to this embodiment and the functional configuration of the information processing device. The information processing system 1 is a system that analyzes and visualizes the flow of objects depicted in moving images, and may, for example, constitute a people flow analysis system that analyzes and visualizes the flow of people in a space such as a store. Note that the information processing system 1 is not limited to systems that analyze the flow of people, and may also be applied to systems that analyze the flow of other moving objects such as vehicles.

[0012] 1, the information processing device 10 functionally comprises an acquisition unit 11, an area setting unit 12, a detection unit 13, a drawing unit 14, and an output unit 15. In the example shown in Fig. 1, the functional units 11 to 15 are configured in one information processing device 10, but they may also be configured in a distributed manner across multiple devices.

[0013] Each functional unit of the information processing device 10 is configured to be able to access a storage unit (storage) such as a video storage unit 21. The video storage unit 21 stores video images captured of a target space, which is a space to be analyzed. In the example shown in FIG. 1 , the video storage unit 21 is configured in the information processing device 10, but may be configured in another device accessible from the information processing device 10.

[0014] Next, a description will be given of each functional unit of the information processing device 10. The acquisition unit 11 acquires moving images captured in a target space. Specifically, the acquisition unit 11 acquires moving images from the moving image storage unit 21.

[0015] The area setting unit 12 sets at least one area in the moving image of the target space as an analysis target area. Specifically, the area setting unit 12 may set the analysis target area based on a designation input.

[0016] 2 is a diagram showing an example of two pairs of analysis target areas set based on an image of a target space to be analyzed and a designation input. The area setting unit 12 receives a designation input for designating the positions of the analysis target areas in an image d1 captured of the target space.

[0017] For example, the area setting unit 12 may set a polygon having vertices at three or more specified points as the analysis target area based on a specified input indicating the positions of three or more points in the image of the target space. Note that the setting of the analysis target area is not limited to being based on a specified input of the positions of the vertices. For example, the area setting unit 12 may set a circular analysis target area based on a specified input indicating the center position and radius of the circle.

[0018] 2, the area setting unit 12 sets a pair of a first analysis target area ar11s and a second analysis target area ar11e based on a designation input. The first analysis target area ar11s and the second analysis target area ar11e constitute a start area and an end area for analyzing people flow between areas.

[0019] Furthermore, the area setting unit 12 may set a plurality of pairs of start and end areas. As shown in Fig. 2, the area setting unit 12 may further set a pair of a first analysis target area ar12s and a second analysis target area ar12e based on a designation input.

[0020] 3 is a diagram showing an example of an image of a target space to be analyzed and four analysis target areas each set based on a specified input. The area setting unit 12 may set a single analysis target area based on the specified input. Alternatively, the area setting unit 12 may set multiple single analysis target areas.

[0021] 3, the area setting unit 12 sets a single analysis target area ar21 based on a designation input. Furthermore, the area setting unit 12 may set single analysis target areas ar22, ar23, and ar24 based on the designation input, respectively.

[0022] The area setting unit 12 may set an analysis pattern based on a specified input in conjunction with the setting of the analysis target area. For example, the area setting unit 12 may set, based on a specified input in conjunction with the setting of a pair of first and second analysis target areas, an analysis pattern to detect people flow from the first analysis target area to the second analysis target area during a time period to be analyzed.

[0023] In addition, in addition to setting a single analysis target area, the area setting unit 12 may set one of the following analysis patterns based on specified input: detecting the flow of people entering the analysis target area, detecting the flow of people leaving the analysis target area, detecting the flow of people detected for the first time in the analysis target area and then leaving the analysis target area, and detecting the presence of people in the analysis target area.

[0024] The detection unit 13 detects a person who is located within the analysis target area during a target time period, which is a time period that is a target of analysis, from a video image that represents the target space. Then, the detection unit 13 tracks the position of the detected person.

[0025] The detection unit 13 may detect a person using any known method, and the type of method is not limited. For example, the detection unit 13 may detect a person from a video by pattern recognition that extracts features such as shape, color, and movement for identifying a person. Alternatively, the detection unit 13 may detect a person using a machine learning model that detects and classifies people in a video.

[0026] The detection unit 13 may track the detected person using any known method. For example, the detection unit 13 may track the person using so-called optical flow. Alternatively, the detection unit 13 may track the person using a method called ByteTrack, which recognizes a person using a bounding box and matches the bounding box between consecutive frames in a time series.

[0027] The drawing unit 14 draws (plots) each time-series position of the person detected by the detection unit 13 by superimposing it on the image of the target space. Specifically, the drawing unit 14 represents the position and movement trajectory of the person not by points and lines but by figures having a certain range.

[0028] The drawing unit 14 may draw a figure having at least a part translucent and a two-dimensional range at each position of a person. By drawing a figure having at least a part translucent at each position of a person in time series, the overlapping of the positions of each person in time series can be visually recognized. Therefore, it is possible to recognize multiple people passing through the same position on a specific route.

[0029] The drawing unit 14 may draw each position of the person by adding, to each pixel of the image in the target space, a pixel value having a magnitude associated with the probability of a two-dimensional Gaussian distribution that takes the average at each position of the person on the surface of the image in the target space.

[0030] That is, the drawing unit 14 may plot each time-series position of the detected person on the image of the target space using a circular figure of a certain size. The circular figure represents a two-dimensional Gaussian distribution, with the largest pixel value at its center and pixel values ​​decreasing with increasing distance from the center. That is, the circular figure has the darkest color at its center and the lightest color with increasing distance from the center.

[0031] More specifically, the rendering unit 14 may add a two-dimensional Gaussian distribution, the average of which is the coordinate of the detected person's position at each time (each frame), to the two-dimensional array of pixel values ​​used in the heat map technique. A heat map is a known method for visualizing two-dimensionally arranged data by visualizing the magnitude of matrix-type numeric data using color values. The two-dimensional Gaussian distribution is expressed by the following equation (1), where (x, y) are the two-dimensional coordinates in the image in the target space. However, in order to make the added value at the coordinates of the person's position 1, equation (1) is normalized as shown in equation (2) below. In this way, by depicting each position of a person over time, it becomes possible to recognize the number of people passing through the same position on a specific route based on the shade of color expressed by the magnitude of pixel values.

[0032] The drawing unit 14 may also plot a triangle having a first position among the positions of the detected person in the time series as the midpoint of the base and a second position, which is a position corresponding to a time later than the first position, as the apex. Fig. 4 is a diagram showing an example of a figure drawn to indicate the position of a person, and is a triangular figure indicating the first and second positions of a moving person in the time series.

[0033] 4, the drawing unit 14 may plot a triangle tr on the image of the target space, with the midpoint of the base being a position hp1(x1, y1) at a first time among the time-series positions of the detected person, and the vertex being a position hp2(x2, y2) at a second time after the first time. Note that the triangle tr may be an acute triangle. Alternatively, the triangle may be an isosceles triangle.

[0034] In this way, by drawing triangles indicating two positions of a person in the time series, the direction of the person's movement can be recognized by drawing one figure.

[0035] 1 , the output unit 15 outputs an image of the target space in which the positions of people are depicted as a people flow information image. Note that the manner in which the people flow information image is output is not limited, and may be displayed on a predetermined display, stored in a predetermined storage device, transmitted to a predetermined device, or the like.

[0036] Next, examples of detecting a person according to an analysis pattern and drawing the position of the person will be described with reference to Figures 5 to 9. In the examples of Figures 5 to 9, the drawing unit 14 draws each position of the person by adding, to each pixel of the image in the target space, a pixel value having a magnitude associated with the probability of a two-dimensional Gaussian distribution that takes the average at each position of the person on the surface of the image in the target space.

[0037] 5 is a diagram showing an example of an image of a target space in which the positions of people moving from a first area to a second area of ​​a pair of analysis target areas are depicted. In the example shown in Fig. 5, the area setting unit 12 sets a first analysis target area ar31 and a second analysis target area ar32. In addition, in response to a designation input, the area setting unit 12 sets, as an analysis pattern, detection of people flow from the first analysis target area ar31 to the second analysis target area ar32.

[0038] The detection unit 13 detects people who move from the first analysis target area ar31 to the second analysis target area ar32 during the target time period in accordance with the set analysis pattern. Then, the drawing unit 14 draws figures f3 representing the respective positions in time series of the multiple people detected by the detection unit 13, superimposed on the image d3 of the target space.

[0039] The output unit 15 outputs an image d3 of the target space on which a figure f3 representing the time-series position of the person is drawn as a people flow information image. By referring to the output people flow information image, it is possible to recognize the flow of people flowing from the first analysis target area ar31 to the second analysis target area ar32.

[0040] 6 is a diagram showing an example of an image of a target space in which the positions of people who have flowed into the analysis target area are depicted. In the example shown in FIG. 6, the area setting unit 12 sets a single analysis target area ar4. In addition, in response to a specified input, the area setting unit 12 sets, as an analysis pattern, detection of a flow of people from outside the analysis target area ar4 into the analysis target area ar4.

[0041] The detection unit 13 detects people who move from outside the analysis target area ar4 into the analysis target area ar4 during the target time period in accordance with the set analysis pattern. Then, the drawing unit 14 draws figures f4 representing the respective positions in time series of the multiple people detected by the detection unit 13, superimposed on the image d4 of the target space.

[0042] The output unit 15 outputs an image d4 of the target space on which a figure f4 representing the time-series position of the person is drawn as a people flow information image. By referring to the output people flow information image, it is possible to recognize the flow of people flowing from outside the analysis target area ar4 into the analysis target area ar4.

[0043] 7 is a diagram showing an example of an image of the target space in which the positions of people who have flowed out of the analysis target area are depicted. In the example shown in Fig. 7, the area setting unit 12 sets a single analysis target area ar5. In addition, in response to a specified input, the area setting unit 12 sets, as an analysis pattern, detection of people flowing from within the analysis target area ar5 to outside the analysis target area ar5.

[0044] The detection unit 13 detects people who move from within the analysis target area ar5 to outside the analysis target area ar5 during the target time period in accordance with the set analysis pattern. Then, the drawing unit 14 draws figures f5 representing the respective positions in time series of the multiple people detected by the detection unit 13, superimposed on the image d5 of the target space.

[0045] The output unit 15 outputs an image d5 of the target space on which a figure f5 representing the time-series position of the person is drawn as a people flow information image. By referring to the output people flow information image, it is possible to recognize the flow of people from within the analysis target area ar5 to outside the analysis target area ar5.

[0046] 8 is a diagram showing an example of an image of the target space in which the positions of people who have been detected for the first time in the analysis target area and have flowed out of the analysis target area are depicted. In the example shown in FIG. 8, the area setting unit 12 sets a single analysis target area ar6. In addition, in response to a designation input, the area setting unit 12 sets, as an analysis pattern, the detection of a flow of people who have been detected for the first time in the analysis target area ar6 and have flowed out of the analysis target area ar6.

[0047] The detection unit 13 detects a person who is detected for the first time in the analysis target area ar6 during the target time period and who has moved outside the analysis target area ar6, according to the set analysis pattern. Then, the drawing unit 14 draws a figure f6 representing each position in time series of the multiple people detected by the detection unit 13, superimposed on the image d6 of the target space.

[0048] The output unit 15 outputs an image d5 of the target space on which a figure f6 representing the time-series position of the person is drawn as a people flow information image. By referring to the output people flow information image, people are first recognized within the analysis target area ar6, and people flowing out of the analysis target area ar6 can be recognized. Therefore, the flow of people flowing into the target space can be recognized from the part of the target space corresponding to the analysis target area ar6.

[0049] 9 is a diagram showing an example of an image of a target space in which the positions of people who have stayed in the analysis target area are depicted. In the example shown in FIG. 9, the area setting unit 12 sets multiple single (not paired) analysis target areas ar71 and ar72. In addition, the area setting unit 12 sets, in response to a designation input, an analysis pattern in which people are detected staying in each of the analysis target areas ar71 and ar72.

[0050] The detection unit 13 detects people who stayed in each of the analysis target areas ar71 and ar72 at each time of the target time period according to the set analysis pattern. Then, the drawing unit 14 draws figures f71 and f72 representing the positions of the multiple people detected by the detection unit 13, associating them with the analysis target areas ar71 and ar72, respectively, and superimposing them on the image d7 of the target space. Note that the detection unit 13 may also detect people who stayed in areas other than the analysis target areas ar71 and ar72. In that case, the drawing unit 14 may draw a figure f7 representing a person outside the analysis target areas on the image d7 of the target space.

[0051] The output unit 15 outputs an image d7 of the target space on which figures f71 and f72 representing the positions of people are drawn as a people flow information image. By referring to the output people flow information image, the length of stay of people in the analysis target areas ar71 and ar72 can be recognized.

[0052] In addition, in the examples of detecting people and drawing their positions described with reference to Figures 5 to 9, the output unit 15 may also output a graph showing the time series change in the number of detected people as a people flow information image.

[0053] FIG. 10 is a flowchart showing the processing content of an information processing method for analyzing and visualizing people flow in a space in the information processing device 10.

[0054] In step S1, the acquisition unit 11 acquires a moving image of the target space. In step S2, the area setting unit 12 sets an analysis target area and an analysis pattern based on, for example, a designation input by a user.

[0055] In step S3, the detection unit 13 detects people from the video and tracks the detected people. That is, the detection unit 13 detects and tracks people from the video who are located in the analysis target area during the target time period, which is the time period to be analyzed, and who match the analysis pattern.

[0056] In step S4, the drawing unit 14 draws the position of the person corresponding to the analysis pattern by superimposing a predetermined figure on the image of the target space.

[0057] In step S5, the output unit 15 outputs an image of the target space in which the time-series positions of people are plotted in a predetermined format as a people flow information image.

[0058] Next, an information processing program for causing a computer to function as the information processing device 10 of this embodiment will be described with reference to Fig. 11. Fig. 11 is a diagram showing the configuration of the information processing program. The information processing program P1 is configured to include a main module m10 that controls information processing in the information processing device 10 in an overall manner, an acquisition module m11, an area setting module m12, a detection module m13, a drawing module m14, and an output module m15. Each of the modules m11 to m15 realizes a function for each of the functional units 11 to 15.

[0059] The information processing program P1 may be transmitted via a transmission medium such as a communication line, or may be stored in a recording medium M1 as shown in FIG.

[0060] According to the information processing system 1, information processing device 10, information processing method, and information processing program P1 of the present embodiment described above, an area is set in a target space, and based on tracking of objects located within the area, each time-series position of the object is drawn superimposed on an image of the target space. This visualizes the movement of the object through the area, making it easy to recognize at least the flow of objects whose specific route is to pass through the area.

[0061] The information processing device and information processing method according to the present disclosure may have the following configurations: The actions and effects of each configuration will be described as follows.

[0062] An information processing device according to one aspect of the present disclosure includes an area setting unit that sets at least one area in a moving image that represents a target space, which is the space to be analyzed; a detection unit that detects specific objects located within the at least one area from the moving image during a target time period, which is the time period to be analyzed, and tracks the detected objects; and a drawing unit that draws each position of the objects detected by the detection unit by superimposing it on an image of the target space.

[0063] An information processing method according to one aspect of the present disclosure is executed by a processor and includes an area setting step of setting at least one area in a moving image representing a target space, which is the space to be analyzed; a detection step of detecting objects located within the at least one area from the moving image during a target time period, which is the time period to be analyzed, and tracking the detected objects; and a drawing step of drawing each position of the objects detected in the detection step superimposed on an image of the target space.

[0064] According to the above aspect, an area is set in a target space, and based on tracking of an object located within the area, each position of the object over time is drawn superimposed on an image of the target space. This visualizes the movement of the object through the area, making it easy to recognize at least the flow of objects whose specific route is to pass through the area.

[0065] In an information processing device according to another aspect, the drawing unit may draw a graphic, at least a portion of which is semi-transparent, at each position of the object.

[0066] According to the above aspect, by drawing at least a partially translucent figure at each position in the time series of the object, the overlapping of the time series positions of the objects can be visually recognized, thereby making it possible to recognize multiple objects that pass through the same position on a specific route.

[0067] In addition, in an information processing device relating to another aspect, the drawing unit may draw each position of the object by adding a pixel value having a magnitude associated with the probability of a two-dimensional Gaussian distribution averaged at each position of the object on the surface of the image of the target space to each pixel of the image of the target space.

[0068] According to the above aspect, it is possible to recognize the amount of objects passing through the same position on a specific route based on the color shading expressed by the magnitude of pixel values.

[0069] In addition, in an information processing device relating to another aspect, the drawing unit may draw a triangle having a first position among the respective positions in the time series of the detected object as the midpoint of the base and a second position, which is a position corresponding to a time later than the first position, as the vertex.

[0070] According to the above aspect, a triangle indicating two positions of an object in time series is drawn, so that the direction of movement of the object can be recognized by drawing one figure.

[0071] In addition, in an information processing device relating to another aspect, the area setting unit may set at least a pair of first and second areas as areas to be analyzed, and the detection unit may detect an object that moves from the first area to the second area during the target time period.

[0072] According to the above aspect, it is possible to recognize the flow of objects flowing from a first area into a second area.

[0073] In addition, in an information processing device relating to another aspect, the area setting unit may set at least one area as an area to be analyzed, and the detection unit may detect an object that moves from outside the area to be analyzed into the area to be analyzed during the target time period.

[0074] According to the above aspect, it is possible to recognize the flow of objects flowing from outside the analysis target area into the analysis target area.

[0075] In addition, in an information processing device relating to another aspect, the area setting unit may set at least one area as an area to be analyzed, and the detection unit may detect an object that moves from within the area to be analyzed to outside the area to be analyzed during the target time period.

[0076] According to the above aspect, it is possible to recognize the flow of objects flowing from within the analysis target area to outside the analysis target area.

[0077] In addition, in an information processing device relating to another aspect, the area setting unit may set at least one area as an area to be analyzed, and the detection unit may detect an object that is detected for the first time within the area to be analyzed during the target time period and that moves from within the area to be analyzed to outside the area to be analyzed.

[0078] According to the above aspect, it is possible to recognize the flow of objects that are first recognized within the analysis target area and then flow out of the analysis target area from within the analysis target area. Therefore, it is possible to recognize the flow of objects flowing into the target space from the part of the target space that corresponds to the analysis target area.

[0079] In an information processing device according to another aspect, the object may be a person.

[0080] According to the above aspect, the movement of people passing through an area is visualized, so that it is possible to easily recognize the flow of people whose specific route is at least passing through the area.

[0081] The block diagram shown in FIG. 2 shows functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (e.g., wired, wireless, etc.) and these multiple devices. The functional block may be realized by combining software with the single device or multiple devices.

[0082] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0083] For example, the information processing device 10 according to an embodiment of the present invention may function as a computer. Fig. 12 is a diagram showing an example of the hardware configuration of the information processing device 10 according to this embodiment. The information processing device 10 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0084] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the information processing device 10 may be configured to include one or more of the apparatuses shown in Fig. 12, or may be configured to exclude some of the apparatuses.

[0085] Each function of the information processing device 10 is realized by loading specified software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations and control communication via the communication device 1004 and the reading and / or writing of data in the memory 1002 and storage 1003.

[0086] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control unit, an arithmetic unit, a register, etc. For example, the functional units 11 to 15 shown in FIG. 1 may be realized by the processor 1001.

[0087] The processor 1001 also reads programs (program codes), software modules, and data from the storage 1003 and / or the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the functional units 11 to 15 of the information processing device 10 may be implemented by a control program stored in the memory 1002 and running on the processor 1001. While the above-described various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented on one or more chips. The programs may also be transmitted from a network via a telecommunications line.

[0088] The memory 1002 is a computer-readable recording medium and may be composed of at least one of, for example, a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), and a random access memory (RAM). The memory 1002 may also be called a register, a cache, a main memory (primary storage device), or the like. The memory 1002 can store executable programs (program codes), software modules, and the like for implementing an information processing method according to one embodiment of the present invention.

[0089] Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including memory 1002 and / or storage 1003.

[0090] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via a wired and / or wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0091] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0092] Furthermore, each device such as the processor 1001 and the memory 1002 is connected to a bus 1007 for communicating information. The bus 1007 may be configured as a single bus, or may be configured as different buses between the devices.

[0093] The information processing device 10 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented by at least one of these pieces of hardware.

[0094] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0095] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (New Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (Ultra-Wide Band), Bluetooth (registered trademark), or other suitable systems, and next-generation systems enhanced based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G, etc.) may also be applied.

[0096] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0097] In the present disclosure, a specific operation described as being performed by a base station may be performed by its upper node in some cases. In a network consisting of one or more network nodes having a base station, it is clear that various operations performed for communication with a terminal may be performed by at least one of the base station and another network node other than the base station (for example, an MME or an S-GW, etc., but are not limited to these). Although the above example illustrates a case where there is one other network node other than the base station, a combination of multiple other network nodes (for example, an MME and an S-GW) may also be used.

[0098] Information etc. may be output from a higher layer (or a lower layer) to a lower layer (or a higher layer), or may be input / output via multiple network nodes.

[0099] Input and output information may be stored in a specific location (for example, memory) or managed in a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0100] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0101] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0102] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0103] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0104] Software, instructions, etc. may also be transmitted or received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies such as coaxial cable, fiber optic cable, twisted pair, and Digital Subscriber Line (DSL), and / or wireless technologies such as infrared, radio, and microwave, these wired and / or wireless technologies are included within the definition of transmission media.

[0105] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0106] It should be noted that terms explained in this disclosure and / or terms necessary for understanding this specification may be replaced with terms having the same or similar meanings.

[0107] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0108] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed as absolute values, relative values ​​from a predetermined value, or other corresponding information. For example, a radio resource may be indicated by an index.

[0109] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0110] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0111] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly specified otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0112] When designations such as "first," "second," etc. are used in this disclosure, any reference to an element does not generally limit the quantity or order of those elements. These designations may be used herein as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed therein or that the first element must precede the second element in some way.

[0113] The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc.

[0114] To the extent that the terms "include," "including," and variations thereof are used herein or in the claims, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, the term "or," as used herein or in the claims, is not intended to be an exclusive or.

[0115] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0116] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0117] The information processing device 10 and the information processing method of the present disclosure may have the following configuration.

[0118] [1] An information processing device comprising: an area setting unit that sets at least one area in a video representing a target space that is a space to be analyzed; a detection unit that detects a specific object located within the at least one area from the video during a target time period that is a time period to be analyzed and tracks the detected object; and a drawing unit that draws each position of the object detected by the detection unit by superimposing it on an image of the target space. [2] The information processing device described in [1], wherein the drawing unit draws a figure that is at least partially semi-transparent at each position of the object. [3] The information processing device described in [2], wherein the drawing unit draws each position of the object by adding, to each pixel of the image of the target space, a pixel value having a magnitude associated with the probability of a two-dimensional Gaussian distribution that takes the average at each position of the object on the surface of the image of the target space. [4] The information processing device described in [2], wherein the drawing unit draws a triangle with a first position among each chronological position of the detected object as the midpoint of the base and a second position corresponding to a time later than the first position as its vertex. [5] The information processing device according to any one of [1] to [4], wherein the area setting unit sets at least a pair of first and second areas as analysis target areas, and the detection unit detects an object that moves from the first area to the second area during the target time period. [6] The information processing device according to any one of [1] to [4], wherein the area setting unit sets at least one area as the analysis target area, and the detection unit detects an object that moves from outside the analysis target area into the analysis target area during the target time period. [7] The information processing device according to any one of [1] to [4], wherein the area setting unit sets at least one area as the analysis target area, and the detection unit detects an object that moves from within the analysis target area to outside the analysis target area during the target time period.[8] The information processing device according to any one of [1] to [4], wherein the area setting unit sets at least one area as an analysis target area, and the detection unit detects an object that is detected in the analysis target area for the first time during the target time period and moves from the analysis target area to outside the analysis target area. [9] The information processing device according to any one of [1] to [4], wherein the area setting unit sets at least one area as the analysis target area, and the detection unit detects an object that has stayed in the analysis target area during the target time period.

[10] The information processing device according to any one of [1] to [9], wherein the object is a person.

[11] An information processing method executed by a processor, comprising: an area setting step of setting at least one area in a video representing a target space that is a space to be analyzed; a detection step of detecting from the video an object that is located in the at least one area during a target time period that is the time period to be analyzed and tracking the detected object; and a drawing step of drawing each position of the object detected in the detection step superimposed on an image of the target space.

[0119] 1...information processing system, 10...information processing device, 11...acquisition unit, 12...area setting unit, 13...detection unit, 14...drawing unit, 15...output unit, 21...moving image storage unit, M1...recording medium, m10...main module, m11...acquisition module, m11 to m15...each module, m12...area setting module, m13...detection module, m14...drawing module, m15...output module, P1...information processing program.

Claims

1. An information processing device comprising: an area setting unit that sets at least one area in a moving image that represents a target space, which is a space to be analyzed; a detection unit that detects specific objects located within the at least one area from the moving image during a target time period, which is a time period to be analyzed, and tracks the detected objects; and a drawing unit that draws each position of the objects detected by the detection unit by superimposing it on an image of the target space.

2. The information processing device according to claim 1, wherein said drawing unit draws a figure, at least a part of which is semi-transparent, at each position of said object.

3. The information processing device according to claim 2, wherein the drawing unit draws each position of the object by adding a pixel value having a magnitude associated with the probability of a two-dimensional Gaussian distribution that takes the average at each position of the object on the surface of the image in the target space to each pixel of the image in the target space.

4. The information processing device according to claim 2, wherein the drawing unit draws a triangle having a first position among the respective positions of the detected object in time series as the midpoint of the base and a second position corresponding to a time later than the first position as the vertex.

5. The information processing device according to claim 1, wherein the area setting unit sets at least a pair of first and second areas as analysis target areas, and the detection unit detects an object that moves from the first area to the second area during the target time period.

6. The information processing device according to claim 1, wherein the area setting unit sets at least one area as an area to be analyzed, and the detection unit detects an object that moves from outside the area to be analyzed into the area to be analyzed during the target time period.

7. The information processing device according to claim 1, wherein the area setting unit sets at least one area as an analysis target area, and the detection unit detects an object that moves from within the analysis target area to outside the analysis target area during the target time period.

8. The information processing device according to claim 1, wherein the area setting unit sets at least one area as an analysis target area, and the detection unit detects an object that is detected for the first time within the analysis target area during the target time period and that moves from within the analysis target area to outside the analysis target area.

9. The information processing device according to claim 1, wherein the object is a person.

10. An information processing method executed by a processor, comprising: an area setting step of setting at least one area in a moving image representing a target space, which is the space to be analyzed; a detection step of detecting objects located within the at least one area from the moving image during a target time period, which is the time period to be analyzed, and tracking the detected objects; and a drawing step of drawing each position of the objects detected in the detection step superimposed on an image of the target space.

Images