Congestion estimation system

Abstract

The objective of the present invention is to estimate congestion in an estimation target location more simply and appropriately.　A congestion estimating system 10 comprises: an image acquiring unit 11 for acquiring an image to be used to estimate congestion; an imaging information acquiring unit 12 for acquiring, on the basis of the acquired image, imaging information indicating a position and imaging direction, at the time the image was captured, of an imaging device that captured the image; a number-of-persons detecting unit 13 for detecting, from the image, the number of persons appearing in the image; and a congestion estimating unit 14 for determining, from the acquired imaging information, a location that is to serve as a congestion estimation target, and estimating the congestion from the detected number of persons, for the determined location.

Description

[Technical field]

[0001] The present invention relates to a congestion degree estimation system that estimates the congestion degree of a location that is an estimation target. [Background technology]

[0002] 2. Description of the Related Art Conventionally, it has been proposed to detect people in an image captured by an imaging device such as a camera and estimate the degree of congestion in the location where the image is captured (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-148422 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional method shown in Patent Document 1, the congestion degree is estimated using an image captured by an imaging device that is fixedly installed in advance at the location where the congestion degree is to be estimated. Therefore, it is necessary to install an imaging device fixedly in advance at the location where the congestion degree is to be estimated. If there are many locations where the congestion degree is to be estimated, it will result in large costs and labor.

[0005] One embodiment of the present invention has been made in consideration of the above, and aims to provide a congestion level estimation system that can more simply and appropriately estimate the congestion level of a location that is the subject of estimation. [Means for solving the problem]

[0006] In order to achieve the above object, a congestion degree estimation system according to an embodiment of the present invention includes: Imaged by a non-fixed imaging device,The system includes an image acquisition unit that acquires an image used to estimate a degree of congestion, an imaging information acquisition unit that acquires, based on the image acquired by the image acquisition unit, imaging information indicating the position and imaging direction of the imaging device that captured the image at the time the image was captured, a number of people detection unit that detects the number of people appearing in the image acquired by the image acquisition unit, and a congestion degree estimation unit that identifies a location to be subject to congestion degree estimation from the imaging information acquired by the imaging information acquisition unit, and estimates the congestion degree for the identified location from the number of people detected by the number of people detection unit.

[0007] In a congestion degree estimation system according to an embodiment of the present invention, a location for which congestion degree is to be estimated is identified from an acquired image, and a congestion degree is estimated for the identified location. Therefore, it is possible to estimate the congestion degree using an image captured by an imaging device other than a fixedly installed imaging device, for example, an image captured by an imaging device carried by a user in the location for which congestion degree is to be estimated. Furthermore, imaging information indicating the position and imaging direction at the time of capturing the image is acquired based on the image, and the location for which congestion degree is to be estimated is identified, so that the location is appropriately identified. Therefore, according to the congestion degree estimation system according to an embodiment of the present invention, it is possible to more simply and appropriately estimate the congestion degree of the location for which congestion degree is to be estimated. Effect of the Invention

[0008] According to one embodiment of the present invention, it is possible to more simply and appropriately estimate the congestion level of a location that is the subject of estimation. [Brief description of the drawings]

[0009] [Figure 1] 1 is a diagram showing a configuration of a congestion degree estimation system according to an embodiment of the present invention. [Diagram 2] 4 is a flowchart showing a process executed in the congestion level estimation system according to the embodiment of the present invention. [Diagram 3] 1 is a diagram illustrating a hardware configuration of a congestion degree estimation system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of a congestion level estimation system according to the present invention will be described in detail with reference to the drawings. In the description of the drawings, the same elements are given the same reference numerals and duplicated description will be omitted.

[0011] FIG. 1 shows a congestion degree estimation system 10 according to the present embodiment. The congestion degree estimation system 10 is a system (device) that estimates the congestion degree of a predetermined place. The congestion degree indicates the degree of congestion by people in a place to be estimated (how many people are in the place). For example, the congestion degree estimation system 10 estimates the congestion degree in a building such as a commercial facility. In this case, the inside of the building is divided into a plurality of areas (sections, measurement points), and each of the plurality of areas is set as a place to be estimated. Each of the plurality of areas may have, for example, a mesh shape. Each of the plurality of areas corresponds to, for example, an individual sales floor in the commercial facility. The congestion degree estimation system 10 manages and outputs information indicating the estimated congestion degree. The information indicating the congestion degree is output so that it can be referred to by, for example, a person who visits the place to be estimated. By referring to the estimated congestion degree, it is possible to know which areas in the commercial facility are crowded and which areas are empty. Note that the places to be estimated for congestion degree are not limited to the inside of the building such as the above-mentioned commercial facility.

[0012] The congestion level estimation system 10 is configured by a computer such as a PC (personal computer) or a server device. The congestion level estimation system 10 may be configured by a plurality of computers, i.e., a computer system. The congestion level estimation system 10 has a communication function and can transmit and receive information to and from other devices.

[0013] The congestion degree estimation system 10 estimates the congestion degree using an image (camera image) captured by the terminal 20 at a location where the congestion degree is to be estimated. The terminal 20 is a device carried and used by a user, for example, a mobile communication terminal such as a smartphone. Alternatively, the terminal 20 may be a device worn by a user, for example, AR (Augmented Reality) glasses (in this case, the terminal 20 is worn on the user's head, specifically, around the eyes). Note that, although only one terminal 20 is depicted in FIG. 2, multiple terminals 20 used by multiple users may be used to estimate the congestion degree.

[0014] The terminal 20 includes an imaging device. The imaging device may be an existing device, such as a camera that is normally provided in a smartphone or the like. In a location where the degree of congestion is to be estimated, the terminal 20 captures an image using an imaging function to obtain an image. The imaging by the terminal 20 in the location where the degree of congestion is to be estimated is performed, for example, at regular intervals (for example, every few minutes). The image obtained by imaging may be a moving image.

[0015] The terminal 20 has a communication function and can transmit and receive information to and from the congestion degree estimation system 10. The communication function may be an existing one. The terminal 20 transmits an image obtained by imaging to the congestion degree estimation system 10. The transmission is performed, for example, every time imaging is performed.

[0016] Next, functions of the congestion level estimation system 10 according to this embodiment will be described. As shown in FIG 1, the congestion level estimation system 10 includes an image acquisition unit 11, an image information acquisition unit 12, a number of people detection unit 13, and a congestion level estimation unit 14.

[0017] The image acquisition unit 11 is a functional unit that acquires an image used for estimating the congestion degree. For example, the image acquisition unit 11 receives an image transmitted from the terminal 20 and acquires the image. The image acquisition unit 11 may also acquire an image by a method other than the above. The image acquisition unit 11 outputs the acquired image to the imaging information acquisition unit 12 and the number of people detection unit 13.

[0018] The imaging information acquisition unit 12 is a functional unit that acquires imaging information indicating the position and imaging direction of the imaging device (i.e., the terminal 20) that captured the image based on the image acquired by the image acquisition unit 11. The imaging information acquisition unit 12 may acquire information indicating a three-dimensional position in a building as information indicating the position at the time of imaging. When the terminal 20 is a device worn by a user, such as AR glasses, and the imaging device provided in the terminal 20 captures an image in the line of sight of the user wearing the terminal 20, the imaging direction is the line of sight of the user.

[0019] Among the acquired imaging information, information indicating the position of the terminal 20 at the time of imaging is, for example, information indicating the coordinates (three-dimensional position) of a three-dimensional coordinate system predefined within a building such as a commercial facility for estimating the congestion level. Among the acquired imaging information, information indicating the imaging direction is, for example, information indicating an absolute angle (orientation) with respect to the north direction. However, the acquired imaging information may be other than the above as long as it is acquired based on an image and is information indicating the position and imaging direction of the terminal 20 at the time of imaging.

[0020] For example, the imaging information acquisition unit 12 acquires imaging information as follows. The imaging information acquisition unit 12 inputs an image from the image acquisition unit 11. The imaging information acquisition unit 12 acquires imaging information based on the image input from the image acquisition unit 11 using existing technology. For example, the imaging information acquisition unit 12 stores information indicating a position and a direction in advance in association with information related to the image, such as feature points. The imaging information acquisition unit 12 extracts information related to the input image, such as feature points of the image input from the image acquisition unit 11. The imaging information acquisition unit 12 compares the extracted feature points, etc. with the stored information related to the image, such as feature points. The imaging information acquisition unit 12 calculates and acquires imaging information related to the image input from the image acquisition unit 11 from the stored association information and the comparison result. In this way, the imaging information acquisition unit 12 may acquire imaging information by a self-location recognition function from an image.

[0021] Furthermore, the imaging information acquisition unit 12 may acquire imaging information by a function external to the congestion degree estimation system 10. For example, the imaging information acquisition unit 12 may transmit an image to an external server (external service) that calculates imaging information from the image, and acquire the imaging information calculated from the image by the external server. The external server may be provided by, for example, GCA or Immersal.

[0022] The imaging information acquisition unit 12 may acquire imaging information based on an image by a method other than the above. The imaging information acquisition unit 12 outputs the acquired imaging information to the congestion degree estimation unit .

[0023] The number of people detection unit 13 is a functional unit that detects the number of people appearing in an image acquired by the image acquisition unit 11. The number of people detection unit 13 inputs an image from the image acquisition unit 11. The number of people detection unit 13 detects the number of people appearing in the image input from the image acquisition unit 11 using existing technology such as image recognition. The number of people detection unit 13 outputs information indicating the detected number of people to the congestion degree estimation unit 14. Note that the imaging information acquired by the imaging information acquisition unit 12 from the same image and the number of people detected by the number of people detection unit 13 are associated with each other.

[0024] The congestion degree estimation unit 14 is a functional unit that identifies a location for which congestion degree is to be estimated from the imaging information acquired by the imaging information acquisition unit 12, and estimates the congestion degree for the identified location from the number of people detected by the number of people detection unit 13. The congestion degree estimation unit 14 may identify any one of a plurality of areas into which a building is divided as the location for which congestion degree is to be estimated. The congestion degree estimation unit 14 may estimate the congestion degree for the same location from the number of people detected by the number of people detection unit 13 from each of a plurality of images.

[0025] The congestion degree estimation unit 14 inputs the imaging information from the imaging information acquisition unit 12. The congestion degree estimation unit 14 inputs information indicating the number of people detected from the number of people detection unit 13. For example, the congestion degree estimation unit 14 estimates the congestion degree as follows, using the imaging information and the information indicating the number of people obtained from the same image.

[0026] The congestion degree estimation unit 14 specifies a position shown in an image related to the imaging information from the imaging information. When the terminal 20 is a device worn by a user, such as AR glasses, and an imaging device provided in the terminal 20 captures an image in the line of sight of the user wearing the terminal 20, the position shown in the image is a position within the visual range of the user. For example, the congestion degree estimation unit 14 specifies a position that is a preset distance (e.g., several meters) in the imaging direction indicated by the imaging information from the position of the terminal 20 at the time of imaging indicated by the imaging information as the position shown in the image related to the imaging information.

[0027] The congestion degree estimation unit 14 stores information indicating the position of each area to be subject to congestion degree estimation in the same coordinate system as the position of the terminal 20 at the time of imaging, which is indicated by the imaging information. The congestion degree estimation unit 14 determines the area including the specified position based on the positions of each area to be subject to congestion degree estimation stored in advance. The congestion degree estimation unit 14 determines the area determined to include the specified position as the area for estimating the congestion degree from the number of people detected by the number of people detection unit 13. The congestion degree estimation unit 14 may determine the number of people detected by the number of people detection unit 13 as the congestion degree of the area. Alternatively, the congestion degree estimation unit 14 may store an equation or condition for estimating the congestion degree from the number of people, and estimate the congestion degree of the area based on the equation or condition. In this case, the estimated congestion degree may be, for example, a numerical value indicating the congestion degree, or may be a qualitative congestion degree (for example, the congestion degree is large, medium, or small).

[0028] The congestion degree estimation unit 14 may store map data indicating the position of an area and associate the estimated congestion degree with the map. That is, the congestion degree estimation unit 14 may plot the congestion degree, such as the number of people, on the map.

[0029] When the congestion degree has already been estimated in an area specified as an area for which the congestion degree is to be estimated from the imaging information, that is, when there is already information indicating the number of people for that area (the information indicating the number of people is duplicated), the congestion degree estimation unit 14 may estimate the congestion degree of that area from the information indicating the existing number of people and the information indicating the new number of people. For example, the congestion degree estimation unit 14 may determine the average of those numbers of people as the congestion degree. In this way, the congestion degree estimation unit 14 may estimate the congestion degree for the same location by adjusting the number of people from the number of people detected by the number of people detection unit 13 from each of the multiple images.

[0030] In order to provide fresh congestion degrees, the congestion degree estimation unit 14 may reset the congestion degree for each area at regular intervals (e.g., every few minutes) or may calculate a new congestion degree using only information indicating the number of people obtained within a certain period of time.

[0031] The congestion degree estimation unit 14 outputs information indicating the estimated congestion degree for each area. For example, the congestion degree estimation unit 14 may transmit information indicating the estimated congestion degree for each area to the terminal 20. Alternatively, the congestion degree estimation unit 14 may transmit information indicating the estimated congestion degree for each area to a display device such as a digital signage installed in a building such as a commercial facility where the congestion degree has been estimated. The information indicating the congestion degree for each area is displayed on these devices that receive the information, so that people in the building can understand the congestion degree for each area. The above transmission may be performed upon request or at regular intervals. In addition, the congestion degree estimation unit 14 may output information indicating the estimated congestion degree for each area to an output destination other than the above by a method other than the above. The above is the function of the congestion degree estimation system 10 according to this embodiment.

[0032] Next, a process executed by the congestion level estimation system 10 according to this embodiment (a method of operation performed by the congestion level estimation system 10) will be described with reference to the flowchart of FIG. 2. In this process, the image acquisition unit 11 acquires an image used for estimating the congestion level (S01). Next, the imaging information acquisition unit 12 acquires imaging information indicating the position and imaging direction of the terminal 20 that captured the image when the image was captured, based on the image (S02). In addition, the number of people detection unit 13 detects the number of people appearing in the image from the image (S03). Note that the process of acquiring imaging information by the imaging information acquisition unit 12 (S02) and the process of detecting the number of people by the number detection unit 13 (S03) can be performed independently of each other, and therefore do not have to be performed in the order shown in FIG. 2.

[0033] Next, the congestion level estimation unit 14 identifies a location for which congestion level is to be estimated from the imaging information (S04). Next, the congestion level estimation unit 14 estimates the congestion level for the identified location from the detected number of people (S05). Next, the congestion level estimation unit 14 outputs information indicating the congestion level estimation result (S06). The above is the process executed by the congestion level estimation system 10 according to this embodiment.

[0034] In this embodiment, a location for which a degree of congestion is to be estimated is specified from an acquired image, and a degree of congestion is estimated for the specified location. Therefore, the degree of congestion can be estimated using an image captured by an imaging device other than a fixedly installed imaging device, for example, an image captured by a terminal 20 carried by a user in a location for which a degree of congestion is to be estimated as in this embodiment. Also, imaging information indicating the position and imaging direction at the time of capturing the image is acquired based on the image, and a location for which a degree of congestion is to be estimated is specified. Therefore, even if the location is indoors, for example, where a position cannot be accurately estimated by a GPS (Global Positioning System), the location is appropriately specified. Therefore, according to this embodiment, the degree of congestion of a location for which an estimation is to be performed can be estimated more simply and appropriately.

[0035] Also, as in the above-described embodiment, information indicating a three-dimensional position in a building may be acquired as information indicating a position at the time of imaging, and one of the areas into which the building is divided may be specified as a location to be estimated for the congestion degree. With this configuration, for example, even in a building such as a multi-story commercial facility, the congestion degree of each area can be appropriately estimated. However, the above configuration does not necessarily have to be adopted for estimating the congestion degree.

[0036] Also, as in the above-described embodiment, the congestion degree for the same location may be estimated from the number of people detected from each of a plurality of images. For example, the number of people may be averaged as described above. With this configuration, the congestion degree can be estimated with high accuracy. However, the congestion degree for the same location may be estimated from one image.

[0037] Some of the above-described functional units included in the congestion level estimation system 10 may be included in another device such as the terminal 20. In this case, the congestion level estimation system may include the other device.

[0038] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. The method of realizing each functional block is not particularly limited. That is, each functional block may be realized by using one device that is physically or logically combined, or may be realized by using two or more devices that are physically or logically separated and directly or indirectly connected (for example, by wire, wirelessly, etc.). The functional blocks may be realized by combining the one device or the multiple devices with software.

[0039] Functions include, but are not limited to, judgment, determination, judgment, 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 the function of transmission is called a transmitting unit or a transmitter. In either case, as described above, there is no particular limitation on the method of realization.

[0040] For example, the congestion level estimation system 10 according to an embodiment of the present disclosure may function as a computer that performs information processing according to the present disclosure. Fig. 3 is a diagram illustrating an example of a hardware configuration of the congestion level estimation system 10 according to an embodiment of the present disclosure. The above-described congestion level estimation system 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, and the like. The hardware configuration of the terminal 20 may also be as described here.

[0041] In the following description, the term "apparatus" may be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the congestion degree estimation system 10 may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.

[0042] Each function in the congestion level estimation system 10 is realized by loading a specific software (program) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication by a communication device 1004, and control at least one of reading and writing of data in the memory 1002 and the storage 1003.

[0043] The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, each function in the congestion degree estimation system 10 described above may be realized by the processor 1001.

[0044] Moreover, the processor 1001 reads out a program (program code), a software module, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to the read out programs. As the program, a program that causes a computer to execute at least a part of the operations described in the above-mentioned embodiment is used. For example, each function in the congestion degree estimation system 10 may be realized by a control program stored in the memory 1002 and operated in the processor 1001. Although the above-mentioned various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may be transmitted from a network via a telecommunication line.

[0045] 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), a random access memory (RAM), etc. The memory 1002 may be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store a program (program code), software modules, etc. that are executable to perform information processing according to an embodiment of the present disclosure.

[0046] The 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, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, and the like. The storage 1003 may be called an auxiliary storage device. The storage medium provided in the congestion degree estimation system 10 may be, for example, a database including at least one of the memory 1002 and the storage 1003, a server, or other appropriate medium.

[0047] The communication device 1004 is hardware (transmission / reception device) for performing communication between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0048] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0049] In addition, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0050] Furthermore, the congestion level estimation system 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), a field programmable gate array (FPGA), etc., and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0051] The order of the steps, sequences, flow charts, 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.

[0052] The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

[0053] The determination may be based on a value represented by a single bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0054] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched according to execution. In addition, notification of specific information (e.g., notification that "X is the case") is not limited to being done explicitly, but may be done implicitly (e.g., not notifying the specific information).

[0055] 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 in the present disclosure. 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.

[0056] 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.

[0057] Additionally, software, instructions, information, etc. may 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 and / or wireless technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave, etc.), then these wired and / or wireless technologies are included within the definition of transmission media.

[0058] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0059] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0060] 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, search, inquiry (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 a memory), and the like. In addition, "judgment" and "decision" can include considering resolving, selecting, choosing, establishing, comparing, etc., to be a "judgment" or "decision." In other words, "judgment" and "decision" can include considering some action to be a "judgment" or "decision." In addition, "judgment" can be interpreted as "assuming," "expecting," "considering," etc.

[0061] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0062] 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."

[0063] Any reference to an element using a designation such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure 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 or that the first element must precede the second element in some way.

[0064] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Further, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0065] In this disclosure, where articles have been added due to translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0066] 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." [Explanation of symbols]

[0067] 10...crowding degree estimation system, 11...image acquisition unit, 12...image information acquisition unit, 13...number of people detection unit, 14...crowding degree estimation unit, 20...terminal, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims

1. An image acquisition unit that acquires an image used for estimating a congestion degree, the image being captured by a non-fixed imaging device; an imaging information acquisition unit that acquires, based on the image acquired by the image acquisition unit, imaging information indicating a position and an imaging direction of an imaging device that captured the image at the time of capturing the image; a number of people detection unit that detects the number of people appearing in the image acquired by the image acquisition unit; a congestion degree estimation unit that identifies a location to be a congestion degree estimation target from the imaging information acquired by the imaging information acquisition unit, and estimates a congestion degree for the identified location from the number of people detected by the number of people detection unit; A congestion level estimation system comprising:

2. The imaging information acquisition unit acquires information indicating a three-dimensional position within a building as information indicating a position at the time of imaging, The congestion level estimation system according to claim 1 , wherein the congestion level estimation unit specifies, as a location for estimating the congestion level, one of a plurality of areas obtained by dividing the building.

3. The crowdedness degree estimation system according to claim 1 , wherein the crowdedness degree estimation unit estimates the crowdedness degree for the same location from the number of people detected from each of a plurality of images by the number of people detection unit.

Images