WO2025169352A1 - Information processing device and information processing method - Google Patents

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

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

Technology is known that obtains the flow of people and their dwell time in a specific area by analyzing images captured of that area. For example, Patent Document 1 describes technology that counts the number and flow of people in a store based on video captured inside the store by a camera installed on the ceiling or other surface of the store.

Japanese Patent Application Laid-Open No. 2006-285409

As one way of understanding people flow, there is a demand for an intuitively recognizable visualization of the flow of objects passing through specific routes within the space being analyzed.

The present disclosure therefore aims to make it easy to recognize the flow of objects along a specific route in a space.

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 video 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 video 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 the positions of the objects detected by the detection unit by superimposing them on an image of the target space.

In accordance with the above aspect, an area is set in a target space, and based on the tracking of objects located within the area, the time-series positions of the objects are drawn superimposed on an image of the target space. This visualizes the movement of objects passing through the area, making it easy to recognize at least the flow of objects that have a specific route passing through the area.

It becomes easier to recognize the flow of objects along a specific route in a space.

FIG. 2 is a block diagram showing the functional configuration of the information processing apparatus according to the present embodiment. FIG. 10 is a diagram showing an example of an image of a target space to be analyzed and two pairs of analysis target areas set based on a specified input. FIG. 10 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. 10 is a diagram showing an example of a graphic drawn to indicate the position of a person, in which a triangular graphic indicates a first position and a second position of a moving person in time series. FIG. 10 is a diagram showing an example of an image of a 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 depicted. FIG. 10 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. FIG. 10 is a diagram showing an example of an image of a target space in which the positions of people who have flown out of the analysis target area are depicted. FIG. 10 is a diagram showing an example of an image of a target space in which the position of a person who has been detected for the first time in the analysis target area and has flown out of the analysis target area is depicted. FIG. 10 is a diagram showing an example of an image of a target space in which the positions of people who stayed in the area to be analyzed are depicted. 10 is a flowchart showing the processing content of an information processing method in the information processing system. FIG. 2 is a diagram showing a configuration of an information processing program. FIG. 2 is a hardware block diagram of the information processing device.

An embodiment of an information processing device according to the present invention will be described with reference to the drawings. Where possible, identical parts will be designated by the same reference numerals, and duplicate explanations will be omitted.

FIG. 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. Information processing system 1 is a system that analyzes and visualizes the flow of objects depicted in moving images, and as an example, can constitute a people flow analysis system that analyzes and visualizes the flow of people in a space such as a store. Note that 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.

As shown in FIG. 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 a single information processing device 10, but they may also be configured distributed across multiple devices.

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

Next, we will explain each functional unit of the information processing device 10. The acquisition unit 11 acquires video images of the target space. Specifically, the acquisition unit 11 acquires video images from the video image storage unit 21.

The area setting unit 12 sets at least one area in the video image of the target space as the analysis target area. Specifically, the area setting unit 12 may set the analysis target area based on a specified input.

FIG. 2 shows an example of two pairs of analysis target areas set based on an image of the target space to be analyzed and a specified input. The area setting unit 12 accepts a specified input for specifying the position of the analysis target area in an image d1 captured of the target space.

The area setting unit 12 may, for example, set a polygon with vertices at three or more specified points as the analysis target area based on 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 specified input of vertex positions. For example, the area setting unit 12 may set a circular analysis target area based on specified input indicating the center position and radius of the circle.

In the example shown in FIG. 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 the specified input. The first analysis target area ar11s and the second analysis target area ar11e constitute the start area and end area for analyzing people flow between areas.

Furthermore, the area setting unit 12 may set multiple 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 the specified input.

FIG. 3 shows an example of an image of the 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. The area setting unit 12 may also set multiple single analysis target areas.

In the example shown in FIG. 3, the area setting unit 12 sets a single analysis target area ar21 based on the specified input. Furthermore, the area setting unit 12 may also set single analysis target areas ar22, ar23, and ar24 based on the specified input.

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 an analysis pattern to detect the flow of people from the first analysis target area to the second analysis target area during the time period to be analyzed based on a specified input in conjunction with the setting of a pair of first and second analysis target areas.

In addition to setting a single analysis target area, the area setting unit 12 may also 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 people staying in the analysis target area.

The detection unit 13 detects people who are located within the analysis target area during the target time period, which is the time period to be analyzed, from the video image depicting the target space. The detection unit 13 then tracks the positions of the detected people.

The detection unit 13 may detect people using any known method, and the type of method is not limited. For example, the detection unit 13 may detect people from video images using pattern recognition that extracts features such as shape, color, and movement to identify people. The detection unit 13 may also detect people using a machine learning model that detects and classifies people in video images.

The detection unit 13 may use any known method to track the detected person. 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 known as ByteTrack, which recognizes the person using a bounding box and matches the bounding box between consecutive frames in a time series.

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

The drawing unit 14 may draw a figure having at least a partially translucent shape and a two-dimensional range at each position of a person. By drawing a figure with at least a partially translucent shape at each position of a person in the time series, the superimposition of each person's position in the time series can be visually recognized. Therefore, it becomes possible to recognize multiple people passing through the same position on a specific route.

The rendering unit 14 may render 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.

In other words, 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 as one moves away from the center. In other words, the circular figure has the darkest color at its center and lighter colors as one moves away from the center.

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.

The drawing unit 14 may also plot a triangle with the first position of the detected person's time series as the midpoint of the base and the second position, which is a position corresponding to a time later than the first position, as the vertex. Figure 4 shows an example of a figure drawn to indicate a person's position, and is a diagram showing a triangular figure indicating the first and second positions of a moving person in the time series.

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

In this way, by drawing triangles that indicate the person's two positions in the time series, the direction of the person's movement can be recognized by drawing a single shape.

Referring again to FIG. 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 specified display, stored in specified storage, transmitted to a specified device, or the like.

Next, with reference to Figures 5 to 9, an example of detecting a person according to an analysis pattern and drawing the person's position will be described. Note that in the examples of Figures 5 to 9, the drawing unit 14 draws each position of a 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.

FIG. 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 specified input, the area setting unit 12 sets the analysis pattern to detect the flow of people from the first analysis target area ar31 to the second analysis target area ar32.

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 according to the set analysis pattern. The drawing unit 14 then draws a figure f3 representing each position in time series of the multiple people detected by the detection unit 13, superimposed on the image d3 of the target space.

The output unit 15 outputs, as a people flow information image, an image d3 of the target space on which a figure f3 representing the person's position over time is drawn. 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.

Figure 6 is a diagram showing an example of an image of the target space in which the positions of people who have flowed into the analysis target area are depicted. In the example shown in Figure 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 the analysis pattern to detect the flow of people from outside the analysis target area ar4 into the analysis target area ar4.

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 according to the set analysis pattern. The drawing unit 14 then draws a figure f4 representing each position in time series of the multiple people detected by the detection unit 13, superimposed on an image d4 of the target space.

The output unit 15 outputs an image d4 of the target space on which a figure f4 representing the person's position over time 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.

Figure 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 Figure 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 the analysis pattern to detect the flow of people from within the analysis target area ar5 to outside the analysis target area ar5.

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 according to the set analysis pattern. The drawing unit 14 then draws a figure f5 representing each position in time series of the multiple people detected by the detection unit 13, superimposed on an image d5 of the target space.

The output unit 15 outputs an image d5 of the target space on which a figure f5 representing the person's position over time 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 within the analysis target area ar5 to outside the analysis target area ar5.

Figure 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 Figure 8, the area setting unit 12 sets a single analysis target area ar6. In addition, in response to a specified input, the area setting unit 12 sets the analysis pattern to detect the 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.

The detection unit 13 detects people who are detected for the first time in the analysis target area ar6 during the target time period and who have moved outside the analysis target area ar6, according to the set analysis pattern. The drawing unit 14 then draws figures f6 representing the chronological positions of the multiple people detected by the detection unit 13, superimposed on the image d6 of the target space.

The output unit 15 outputs, as a people flow information image, an image d5 of the target space on which a figure f6 representing the person's position over time is drawn. By referencing the output people flow information image, people are first recognized within the analysis target area ar6, and the flow of people flowing out of the analysis target area ar6 from within 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 that corresponds to the analysis target area ar6.

Figure 9 is a diagram showing an example of an image of the target space in which the positions of people who have stayed in the analysis target area are depicted. In the example shown in Figure 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 the analysis pattern to detect the presence of people in each of the analysis target areas ar71 and ar72 in response to the specified input.

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. The drawing unit 14 then associates figures f71 and f72 representing the positions of the multiple people detected by the detection unit 13 with each of the analysis target areas ar71 and ar72, and draws them superimposed 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 figures f7 representing people outside the analysis target areas on the image d7 of the target space.

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 areas ar71 and ar72 under analysis can be determined.

In the examples of detecting people and depicting 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.

Figure 10 is a flowchart showing the processing details of an information processing method for analyzing and visualizing people flow in a space using the information processing device 10.

In step S1, the acquisition unit 11 acquires a moving image of the target space. In step S2, the area setting unit 12 sets the analysis target area and analysis pattern based on, for example, a user's designated input.

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 within the analysis target area during the target time period, which is the time period to be analyzed, and who match the analysis pattern.

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.

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.

Next, with reference to FIG. 11, an information processing program for causing a computer to function as the information processing device 10 of this embodiment will be described. FIG. 11 is a diagram showing the configuration of the information processing program. The information processing program P1 is composed of a main module m10 that controls overall information processing in the information processing device 10, 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 then realizes the functions for the respective functional units 11 to 15.

In addition, the information processing program P1 may be transmitted via a transmission medium such as a communication line, or may be stored on a recording medium M1 as shown in FIG. 11.

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

The information processing device and information processing method according to the present disclosure may have the following configurations. The functions and effects of each configuration are explained as follows:

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 the positions of the objects detected by the detection unit by superimposing them on an image of the target space.

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 the positions of the objects detected in the detection step superimposed on an image of the target space.

In accordance with the above aspect, an area is set in a target space, and based on the tracking of objects located within the area, the time-series positions of the objects are drawn superimposed on an image of the target space. This visualizes the movement of objects passing through the area, making it easy to recognize at least the flow of objects that have a specific route passing through the area.

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

In accordance with the above aspect, by drawing at least a partially translucent figure at each position in the object's time series, the superimposition of the object's time series positions can be visually recognized. This makes it possible to recognize multiple objects that pass through the same position on a specific route.

Furthermore, in an information processing device according to another aspect, the drawing unit may draw each position of the object 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 object on the surface of the image in the target space.

The above aspect makes it possible to recognize the amount of objects passing through the same position on a specific route based on the color shades expressed by the magnitude of pixel values.

Furthermore, in an information processing device according to another aspect, the drawing unit may draw a triangle having a first position among the positions of the detected object 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 its vertex.

In accordance with the above aspect, a triangle is drawn to indicate two positions of the object in the time series, so the direction of the object's movement can be recognized by drawing a single shape.

Furthermore, in an information processing device according to another aspect, the area setting unit may set at least a pair of first and second areas as the analysis target areas, and the detection unit may detect an object that moves from the first area to the second area during the target time period.

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

Furthermore, in an information processing device according to another aspect, the area setting unit may set at least one area as an analysis target area, and the detection unit may detect an object that moves from outside the analysis target area into the analysis target area during the target time period.

The above aspects make it possible to recognize the flow of objects flowing from outside the analysis area into the analysis area.

Furthermore, in an information processing device according to another aspect, the area setting unit may set at least one area as an analysis target area, and the detection unit may detect an object that moves from within the analysis target area to outside the analysis target area during the target time period.

The above aspects make it possible to recognize the flow of objects flowing from within the analysis area to outside the analysis area.

Furthermore, in an information processing device according to another aspect, the area setting unit may set at least one area as an analysis target area, and the detection unit may detect an object that is detected for the first time within the analysis target area during the target time period and then moves from within the analysis target area to outside the analysis target area.

The above aspects allow for the recognition of the flow of objects that are first recognized within the analysis target area and then flow out of the analysis target area. Therefore, the flow of objects flowing into the target space can be recognized from the part of the target space that corresponds to the analysis target area.

In another aspect of the information processing device, the object may be a person.

The above aspects make it possible to visualize the movement of people passing through an area, making it easy to recognize people flowing along specific routes, at least through the area.

Note that the block diagram shown in Figure 2 shows functional blocks. These functional blocks (components) are realized by any combination of hardware and/or software. Furthermore, there are no particular limitations on how each functional block is realized. 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 connected directly or indirectly (for example, using wires, wirelessly, etc.) and these multiple devices. A functional block may also be realized by combining software with the single device or multiple devices.

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

For example, the information processing device 10 in one embodiment of the present invention may function as a computer. Figure 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, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

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

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

The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) that includes an interface with peripheral devices, a control unit, an arithmetic unit, registers, etc. For example, the functional units 11 to 15 shown in Figure 1 may be realized by the processor 1001.

In addition, the processor 1001 reads programs (program code), 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-mentioned 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 executed by the processor 1001. While the above-mentioned various processes have been described as being executed by a single 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.

Memory 1002 is a computer-readable recording medium and may be composed of at least one of, for example, ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. Memory 1002 may also be called a register, cache, main memory (primary storage device), etc. Memory 1002 can store executable programs (program code), software modules, etc. for implementing an information processing method according to one embodiment of the present invention.

Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, 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, 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 suitable medium including memory 1002 and/or storage 1003.

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

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

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

Furthermore, the information processing device 10 may 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 this hardware. For example, the processor 1001 may be implemented by at least one of these pieces of hardware.

The notification of information is not limited to the aspects/embodiments described in this disclosure and may be performed using other methods. For example, the notification of information may be performed using physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), higher layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or a combination of these. Furthermore, 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, etc.

Each aspect/embodiment described in this disclosure may be applied to at least one of systems utilizing 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-WideBand), Bluetooth (registered trademark), or other suitable systems, and next generation systems enhanced based on these. Additionally, multiple systems may be combined (for example, a combination of at least one of LTE and LTE-A with 5G).

The order of the processing steps, sequences, flowcharts, etc. of each aspect/embodiment described in this disclosure may be changed unless inconsistent. 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.

In this disclosure, certain operations described as being performed by a base station may in some cases be performed by its upper node. In a network consisting of one or more network nodes having base stations, it is clear that various operations performed for communication with terminals may be performed by at least one of the base station and other network nodes other than the base station (such as, but not limited to, an MME or S-GW). While the above example illustrates a case where there is one other network node other than the base station, it may also be a combination of multiple other network nodes (for example, an MME and an S-GW).

Information, etc. can be output from a higher layer (or lower layer) to a lower layer (or higher layer). It may also be input/output via multiple network nodes.

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.

The determination may be made based on a value represented by a single bit (0 or 1), a Boolean value (true or false), or a numerical comparison (for example, a comparison with a predetermined value).

Each aspect/embodiment described in this disclosure may be used alone, in combination, or switched between depending on the implementation. Furthermore, notification of specified information (e.g., notification that "X is true") is not limited to being done explicitly, but may also be done implicitly (e.g., not notifying the specified information).

Although the present disclosure has been described in detail above, it will be 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, which are defined by the claims. Therefore, the description of the present disclosure is intended for illustrative purposes only and does not have any limiting meaning on the present disclosure.

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.

Software, instructions, etc. may also be transmitted and received via a transmission medium. For example, if 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.

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.

Note 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.

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

Furthermore, the information, parameters, etc. described in this disclosure may be expressed as absolute values, as relative values from a predetermined value, or as corresponding other information. For example, radio resources may be indicated by an index.

The names used for the parameters described above are not intended to be limiting in any way. Furthermore, the mathematical formulas 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.

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 a table, database, or other data structure), and ascertaining something that is considered to be a "determination." "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 so on. Furthermore, "judgment" and "decision" can include regarding actions such as resolving, selecting, choosing, establishing, and comparing as having been "judgment" or "decision." In other words, "judgment" and "decision" can include regarding some action as having been "judgment" or "decision." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

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

When designations such as "first," "second," etc. are used in this disclosure, any reference to such elements 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.

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

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.

In this disclosure, where articles are added by translation, such as a, an, and the in English, this disclosure may include the noun following these articles being plural.

In this disclosure, the term "A and B are different" may mean "A and B are different from each other." Note that this term may also mean "A and B are each different from C." Terms such as "separate" and "combined" may also be interpreted in the same way as "different."

The information processing device 10 and information processing method disclosed herein may have the following configuration.

[1]
an area setting unit that sets at least one area in a moving image that represents 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 during a target time period that is a time period to be analyzed from the video and tracks the detected object;
a drawing unit that draws each position of the object detected by the detection unit so as to be superimposed on the image of the target space;
An information processing device comprising:
[2]
The information processing device according to [1], wherein the drawing unit draws a figure, at least a portion of which is semi-transparent, at each position of the object.
[3]
the rendering unit renders each position of the object by adding, to each pixel of the image in the object space, a pixel value having a magnitude associated with a probability of a two-dimensional Gaussian distribution that takes an average at each position of the object on the surface of the image in the object space;
The information processing device according to [2].
[4]
the drawing unit draws a triangle having a first position among the respective positions of the detected object in time series as a midpoint of a base and a second position corresponding to a time later than the first position as a vertex;
The information processing device according to [2].
[5]
the area setting unit sets at least a pair of first and second areas as analysis target areas;
the detection unit detects an object that moves from the first area to the second area during the target time period.
[1] - [4] The information processing device according to any one of the above.
[6]
the area setting unit sets at least one area as an analysis target area;
the detection unit detects an object that moves from outside the analysis target area into the analysis target area during the target time period;
[1] - [4] The information processing device according to any one of the above.
[7]
the area setting unit sets at least one area as an analysis target area;
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;
[1] - [4] The information processing device according to any one of the above.
[8]
the area setting unit sets at least one area as an analysis target area;
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 has moved from within the analysis target area to outside the analysis target area;
[1] - [4] The information processing device according to any one of the above.
[9]
the area setting unit sets at least one area as an analysis target area;
the detection unit detects an object that has stayed in the analysis target area during the target time period;
[1] - [4] The information processing device according to any one of the above.
[10]
The information processing device according to any one of [1] to [9], wherein the object is a person.
[11]
an area setting step of setting at least one area in a moving image representing a target space that is a space to be analyzed;
a detection step of detecting an object located within the at least one area during a target time period that is a time period to be analyzed from the video and tracking the detected object;
a drawing step of drawing each position of the object detected in the detection step so as to be superimposed on the image of the target space;
An information processing method executed by a processor, comprising:

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-m15...each module, m12...area setting module, m13...detection module, m14...drawing module, m15...output module, P1...information processing program.

Claims (10)

an area setting unit that sets at least one area in a moving image that represents 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 during a target time period that is a time period to be analyzed from the video and tracks the detected object;
a drawing unit that draws each position of the object detected by the detection unit so as to be superimposed on the image of the target space;
An information processing device comprising: The information processing device according to claim 1, wherein the drawing unit draws a figure that is at least partially semi-transparent at each position of the object. the rendering unit renders each position of the object by adding, to each pixel of the image in the object space, a pixel value having a magnitude associated with a probability of a two-dimensional Gaussian distribution that takes an average at each position of the object on the surface of the image in the object space;
The information processing device according to claim 2 . the drawing unit draws a triangle having a first position among the respective positions of the detected object in time series as a midpoint of a base and a second position corresponding to a time later than the first position as a vertex;
The information processing device according to claim 2 . the area setting unit sets at least a pair of first and second areas as analysis target areas;
the detection unit detects an object that moves from the first area to the second area during the target time period.
The information processing device according to claim 1 . the area setting unit sets at least one area as an analysis target area;
the detection unit detects an object that moves from outside the analysis target area into the analysis target area during the target time period;
The information processing device according to claim 1 . the area setting unit sets at least one area as an analysis target area;
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;
The information processing device according to claim 1 . the area setting unit sets at least one area as an analysis target area;
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 has moved from within the analysis target area to outside the analysis target area;
The information processing device according to claim 1 . The information processing device of claim 1, wherein the object is a person. an area setting step of setting at least one area in a moving image representing a target space that is a space to be analyzed;
a detection step of detecting an object located within the at least one area during a target time period that is a time period to be analyzed from the video and tracking the detected object;
a drawing step of drawing each position of the object detected in the detection step so as to be superimposed on the image of the target space;
An information processing method executed by a processor, comprising: