JP2023023492A - Image sensor system, calculation device, sensing method and program - Google Patents

Abstract

To provide an image sensor system capable of calculating a use rate of a space.SOLUTION: An image sensor system includes a plurality of image sensors installed in an object space, and a calculation device. The image sensor includes an imaging section for acquiring image data of an allocated area, and a processor for generating space use data showing the distribution of a moving body in the area after image processing of the image data. The calculation device includes an acquisition section, a storage section, an integration processing section and a space use rate calculation section. The acquisition section acquires the space use data from the image sensor. The storage section stores map data showing a correspondence relation of an object space with the area. The integration processing section integrates the acquired space use data on the basis of the map data to generate space use data in the object space. The space use rate calculation section calculates a space use rate which is an index showing a degree of using the object space by the moving body for each place.SELECTED DRAWING: Figure 5

Description

Embodiments of the present invention relate to image sensor systems, computing devices, sensing methods, and programs.

A recent image sensor is equipped with a CPU (Central Processing Unit) and memory, and can be said to be a so-called built-in computer with a lens. It also has advanced image processing functions, and can analyze the captured image data and calculate the presence/absence of people moving within the field of view, or the number of people. An object that can be detected by moving within the field of view of the image sensor is hereinafter referred to as a moving object. For example, the moving object is a living object such as a person or an animal, a machine (such as a facsimile machine) having an operating part, or a non-living moving object (such as a vehicle (including an unmanned guided vehicle)).

Japanese Patent No. 6616521 JP 2019-200715 A JP 2010-117216 A JP 2012-146022 A Japanese Unexamined Patent Application Publication No. 2013-137701

The people flow data is an index that converts the degree of movement of moving bodies into data. By using people flow data, it is possible to create information that is effective for visual confirmation of people (heat map), by expressing places where there is a lot of movement of people in dark colors and places where there is little movement in light colors. . In other words, people flow data is data obtained by accumulating how individual people move, and it can be said that people are the subject of discussion.

From a different point of view, there is an increasing need to quantify the extent to which a moving object uses space. For example, it is considered to create a more comfortable space by calculating the degree to which a person stays in place as a numerical value and applying that value to an air conditioning or lighting control system. However, there is no known technology that enables numerical calculation of the degree to which a moving object uses space. With existing technology, only indices such as density and degree of congestion are known, and indices that can be directly input to devices and can be called space utilization rates of moving objects cannot be obtained. Furthermore, there is no known technique for obtaining an index related to a large space that exceeds the detection range of a single sensor.

Accordingly, it is an object of the present invention to provide an image sensor system, a computing device, a sensing method, and a program capable of calculating the usage rate of space.

According to an embodiment, an image sensor system includes a plurality of image sensors installed in a target space and a computing device in communication with the image sensors. The image sensor includes an imaging unit that acquires image data for each assigned area, and a processor that processes the image data to generate space usage data that indicates the distribution of moving objects in the area. The computing device includes an acquisition unit, a storage unit, an integration processing unit, and a space usage rate calculation unit. The acquisition unit acquires space usage data from multiple image sensors. The storage unit stores map data indicating the correspondence relationship between the target space and the area. The integration processing unit integrates the acquired space usage data based on the map data to generate space usage data in the target space. The space usage rate calculation unit calculates a space usage rate, which is an index indicating the degree to which the moving body uses the target space for each location, based on the space usage data in the target space.

FIG. 1 is a schematic diagram showing an example of an image sensor system according to an embodiment. FIG. 2 is a diagram showing an example inside a building floor. FIG. 3 is a block diagram showing an example of the image sensor system shown in FIG. FIG. 4 is a block diagram showing an example of the image sensor 3 according to the embodiment. FIG. 5 is a block diagram showing an example of the gateway device 7 according to the embodiment. FIG. 6 is a flow chart showing an example of a processing procedure of the image sensor 3. As shown in FIG. FIG. 7 is a diagram showing an example of an image within the field of view of the imaging section 31. As shown in FIG. FIG. 8 is a diagram showing an example of data flow in the image sensor 3. As shown in FIG. FIG. 9 is a flow chart showing an example of the processing procedure of the gateway device 7. As shown in FIG. FIG. 10 is a schematic diagram showing an example of the space usage rate. FIG. 11 is a diagram showing an example of data flow in the gateway device 7. As shown in FIG. FIG. 12 is a diagram showing an example of a heat map based on the analysis result of space utilization. FIG. 13 is a diagram showing an example of a color map based on the analysis result of space utilization.

An image sensor can acquire a wider variety of information than a human sensor, a light sensor, an infrared sensor, or the like. By using a wide-angle lens such as a fisheye lens, the area that can be captured by a single image sensor can be expanded. Image distortion can be corrected by computational processing. Image sensors are also known that have a function of masking an area in the field of view that is not desired to be sensed, and have a learning function. Various sensing data can be obtained by processing image data captured by a fisheye camera. Image sensors with communication functions are also known, and have a high affinity with IoT (Internet of Things) technology. Furthermore, it is also expected to be an edge device for big data analysis. An example of a system using this type of sensor will be described below.

<Configuration>
FIG. 1 is a schematic diagram illustrating an example of an image sensor system including an image sensor according to an embodiment. In FIG. 1, lighting equipment 1, lighting controller 4, and image sensors 3 (3-1 to 3-n) are provided, for example, on each floor of building 400, and are connected to gateway device 7 so as to be communicable. The gateway device 7 on each floor is communicably connected to, for example, a building management center BEMS (Building Energy Management System) server 5 via an intra-building network 8 .

The BEMS server 5 can be connected to a cloud computing system (cloud) 100 via a TCP/IP (Transmission Control Protocol/Internet Protocol) based communication network 10, for example. The cloud 100 includes a server 200 and a database 300, and provides services related to building management and the like.

FIG. 2 is a diagram showing an example of floors in the building 400. As shown in FIG. As shown in FIG. 2, the lighting equipment 1, the outlets of the air conditioners 2, and the image sensor 3 are arranged on, for example, the ceiling of each floor. Each image sensor 3 is assigned an area to be sensed. In FIG. 2 it is shown that areas A1 and A2 each cover approximately half of the floor. All areas together can cover the floor of the target space. As indicated by hatching in the drawing, the areas assigned to different image sensors 3 may partially overlap.

The image sensor 3 looks down on the assigned area and catches it within the field of view at an angle of view, and acquires image data by capturing an image within the field of view. This image data is processed by the image sensor 3 to generate various sensing data such as human information or environmental information. That is, the image sensor 3 generates sensing data of the area assigned to itself.

The environment information is information about the environment of the area, such as the illuminance and temperature of the floor. Person information is information about a person in an area. Examples of personal information include the number of people in an area, behavior of people, amount of activity of people, presence/absence indicating the presence or absence of people, and the like. In recent years, it has been studied to improve the comfort and safety of people in living environments by controlling lighting equipment and air conditioning equipment based on such information. It is also possible to calculate environmental information and person information for each small area obtained by dividing the target space into a plurality of areas. This small area may be associated with the allocated area for each image sensor 3 .

The embodiment introduces a quantity called space utilization. The space usage rate is an index indicating the degree (usage rate) to which the moving body uses the space. For example, the space utilization rate is an index that indicates the degree to which people stay in each place. Using the space may mean, for example, sitting at a seat and doing office work. Alternatively, it may be possible to move through passages or stairs in the space. Alternatively, it may be walking around the sales floor in a store or the like.

FIG. 3 is a block diagram showing an example of the image sensor system shown in FIG. In FIG. 3, a gateway device 7, a management terminal 20, a BEMS server 5, and a lighting controller 4 are connected to an intra-building network 8 to form a building system. A typical communication protocol for the intra-building network 8 is the Building Automation and Control Networking protocol (BACnet (registered trademark)). In addition, protocols such as DALI, ZigBee (registered trademark), and ECHONET Lite (registered trademark) are also known.

The gateway device 7 accommodates a plurality of image sensors 3 (3-1 to 3-n) as subordinates. The image sensors 3-1 to 3-n are connected by a sensor network 9 in a unicursal manner (crossover wiring). The sensor network 9 connects the image sensors 3-1 to 3-n and the gateway device 7 so as to be able to communicate with each other. EtherCAT (registered trademark), for example, is known as a protocol for the sensor network 9 .

The gateway device 7 converts communication protocols between the sensor network 9 and the in-building network 8 . Therefore, the BEMS server 5 and the management terminal 20 can mutually communicate with the image sensors 3-1 to 3-n. The BEMS server 5 acquires sensing data from the image sensors 3-1 to 3-n and controls the air conditioning and lighting of the building 400. FIG.

The management terminal 20 individually accesses the image sensors 3-1 to 3-n to input commands and perform various settings. The lighting controller 4 controls the lighting equipment 1 based on control from the BEMS server 5 .

FIG. 4 is a block diagram showing an example of the image sensor 3 according to the embodiment. The image sensor 3 includes an imaging section 31 , a memory 32 , a processor 33 and a communication section 34 . These are connected to each other via an internal bus 35 . Image sensor 3 with memory 32 and processor 33 is a computer.

The imaging unit 31 is a so-called fish-eye camera, and includes a fish-eye lens 31a as a wide-angle lens, a diaphragm mechanism 31b, an image sensor 31c, and a register 30. The fish-eye lens 31a captures the area in the view looking down from the ceiling (downward angle of view) and forms an image on the image sensor 31c. The amount of light from the fisheye lens 31a is adjusted by a diaphragm mechanism 31b. The image sensor 31c is, for example, a CMOS (Complementary Metal Oxide Semiconductor) sensor, and generates a video signal with a frame rate of, for example, 30 frames per second. This video signal is digitally encoded and output as original image data. This image data is stored in the memory 32 (image data 32b).

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

The memory 32 is a semiconductor memory such as SDRAM (Synchronous Dynamic RAM), or a non-volatile memory such as NAND flash memory or EPROM (Erasable Programmable ROM). The memory 32 stores a program 32a for causing the processor 33 to function, image data 32b acquired by the imaging unit 31, and parameters 32c.

The processor 33 is, for example, a multi-core CPU (Central Processing Unit) and is an LSI (Large Scale Integration) tuned to execute image processing at high speed. The processor 33 can also be configured with an MPU (Micro Processing Unit), an FPGA (Field Programmable Gate Array), or the like. The processor 33 executes a program 32a read into an internal register (not shown) to realize various functions described in the embodiments.

The communication unit 34 can be connected to the sensor network 9, and mediates the exchange of data with a communication partner (the gateway device 7, the management terminal 20, another image sensor 3, or the BEMS server 5, etc.). The communication interface may be wired or wireless. Arbitrary topologies such as line wiring, ring wiring, and tree wiring can be applied to the topology of the communication network. The communication protocol may be a general purpose protocol or an industrial protocol. A single communication method may be used, or a plurality of communication methods may be combined. The object of communication may be either real-time data or stored data. Furthermore, it is also possible to select constant communication, on-demand communication, or the like.

In particular, the communication unit 34 receives sensing data from the image sensor 3, processing results of the processor 33, processing data, map information, parameters related to image analysis, various images (heat map, etc.) used as a basis for calculating the space utilization rate, Various images such as original images and processed images, various parameters such as space usage rate calculation algorithms and layout maps, or various parameters for other events (dictionary data, etc.) are sent and received via the sensor network 9 as a communication network. do. As a result, the data and information can be shared with the gateway device 7, other image sensors 3, the BEMS server 5, the management terminal 20, and the like via the intra-building network 8 and the like.

By the way, the processor 33 has a space usage data generator 33a as a processing function according to the embodiment. The space use data generation unit 33a can be understood as a process generated by loading the program 32a of the memory 32 into the register of the processor 33 and executing arithmetic processing by the processor 33 as the program progresses.

The space usage data generation unit 33a performs image processing on the image data 32b in the memory 32, and detects areas in which people exist (person areas) for each of a plurality of frames. The human region is accumulated over multiple frames. Then, the space use data generation unit 33a generates space use data as an average value between frames for a predetermined accumulation time, for example. Here, the accumulation time can be set to any value from the management terminal 20, for example, and registered in the parameter 32c. Space usage data may be represented as array data, or may be represented as an image such as a heat map.

As image processing, techniques such as background subtraction, inter-frame subtraction, template matching, machine learning, or deep learning can be applied. The unit of the person region can be arbitrarily set, for example, in pixel units, block units, area units, the entire image, or a mixture thereof. This can also be arbitrarily changed from the management terminal 20, for example, and the set value is registered in the parameter 32c.

A parameter is an index for associating the field of view of the imaging unit 31 with an area on the real space, and can be stored in a plurality of types in the embodiment. Examples of parameters include numerical values such as the ceiling height and detection range of the target space, a floor layout map, and a layout map divided into areas. It is appropriately read and used by the processor 33 .

FIG. 5 is a functional block diagram showing an example of the gateway device 7 according to the embodiment. The gateway device 7 as an example of a computing device is a computer including a processor 75 such as a CPU or MPU (Micro Processing Unit), a ROM (Read Only Memory) 72 and a RAM (Random Access Memory) 73 . The gateway device 7 further includes a storage section 74 and a communication section 77 .

The ROM 72 stores an embedded OS (Operating System), basic programs such as various types of firmware, various setting data, and the like. The RAM 73 temporarily stores programs and data loaded from the storage unit 74 .

The communication unit 77 has a function for communicating with the image sensor 3, the BEMS server 5, etc. via the intra-building network 8. FIG. Various programs to be executed by the gateway device 7 can be downloaded from a server via the communication unit 77 and installed in the storage unit 74, for example. It is also possible to access the cloud 100 (FIG. 1) from the communication unit 77 via the intra-building network 8, download the latest programs, and update the internal programs.

In addition to the program 74a executed by the processor 75, the storage unit 74 stores map data 74b, space usage data 74c, space usage rate 74d, and algorithm 74e.

The map data 74b is data indicating the correspondence relationship between the assigned area for each image sensor 3 and the map of the target space.

Space usage data 74c is space usage data collected from image sensors 3-1 through 3-n. Image sensors 3-1 to 3-n transmit space usage data calculated for their assigned areas. The gateway device 7 that has collected this integrates the space use data of each sensor and converts it into space use data regarding the target space.

A space utilization rate 74 d is a space utilization rate calculated by the processor 75 . Space utilization 74d is accumulated over a predetermined period of time.
Algorithm 74e is an algorithm used by processor 75 to calculate space utilization.

The processor 75 executes various programs. The processor 75 also includes an acquisition unit 75a, an integration processing unit 75b, a space utilization calculation unit 75c, a prediction unit 75d, an analysis unit 75e, and a visualization unit 75f as processing functions according to the embodiment. These functional blocks can be understood as processes generated in the course of program 74a being loaded into RAM 73 and the program being executed. That is, the program 74a causes the gateway device 7, which is a computer, to operate as an acquisition unit 75a, an integration processing unit 75b, a space utilization calculation unit 75c, a prediction unit 75d, an analysis unit 75e, and a visualization unit 75f.

The acquisition unit 75a acquires space usage data as sensing data detected by each of the image sensors 3-1 to 3-n. The acquired space use data is stored and accumulated in the storage unit 74 (space use data 74c).
The integration processing unit 75b integrates the space usage data 74c based on the map data 74b to generate space usage data in the target space.

The space usage rate calculator 75c calculates the space usage rate based on the space usage data 74c obtained above. Space utilization can be defined in various ways. That is, various space utilization rates can be calculated based on different algorithms (functions). Individual algorithms are stored in the storage unit 74 (algorithm 74c) and read by the processor 75 for use. Which algorithm is used to calculate the space usage rate can be arbitrarily set from the management terminal 20, for example.

For example, a unit of calculation can be cited as an example of different definitions of space utilization. In other words, it is possible to define whether the space utilization rate is calculated in units of the entire target space or in units of individual blocks. A block may be, for example, each seat, or may be defined as a unit in which a plurality of blocks are grouped together. Information such as how to set blocks can be arbitrarily set from the management terminal 20, for example.

Also, the denominator and numerator in the calculation formula can be set to different amounts to define a plurality of space usage rates. For example, if the denominator is the area on the image or the area on the real space, at least two types of space usage rates can be defined. Furthermore, the denominator may be defined as the area of the entire image or area in which a person has existed in the past. In doing so, it is also possible to define a reference time such as the average value of the past day, the average value of the past week, the average value of the past month, the average value of each season, or the average value of a year. . The reference time to the denominator may be the same as or different from the accumulated time when calculating space utilization. The amount applied to the denominator and the reference time can be arbitrarily set from the management terminal 20, for example.

As the numerator, for example, the area of the person region can be applied. One can also apply one or more conditionings to the person and define the numerator as the area of one or more person regions. Examples of conditioning include, for example, magnitude of movement (person working in an office, person walking, etc.). In addition, posture (person standing, sitting, etc.), clothing (person wearing uniform, person wearing suit, etc.), attributes (gender, age), etc. can also be used as examples of conditioning. can. Furthermore, by specifying an individual within the field of view and using the area of each individual as a numerator, it is possible to calculate the space usage rate of the individual.

In addition, as an example of the definition of the space utilization rate, space units (zones, floors, multiple floors, entire buildings, organizational units (sections, departments), etc., areas where people have lived in the past) or time units (hours, days , week, month, year, business day, holiday, etc.) can be set.

Furthermore, the processor 75 sets the area for which the space usage rate is to be calculated in the target space. For example, it is possible to set an area for each chair on the office floor, and define the space usage rate as the amount (seating rate) indicating how long people are sitting in each chair. A technique such as semantic segmentation, for example, can be applied to the setting of the area.

The prediction unit 75d predicts the future space usage rate based on the accumulated space usage rate 74d. For example, by predicting tomorrow's space utilization rate, tomorrow's crowded places can be predicted.

Based on the accumulated space usage rate 74d, the analysis unit 75e analyzes the trend of the space usage rate. By analyzing the space utilization rate integrated over the target space, for example, it is possible to extract significant trends such as congestion in a specific place on a specific day of the week, a specific season, or a specific day.

The visualization unit 75f visualizes the space usage rate calculated by the processor 75, the future space usage rate, or the tendency of the space usage rate, so as to visually appeal to the user. Visualization processing may include, for example, graphing the space utilization rate or heat mapping.

<Action>
Next, the operation of the above configuration will be described.
FIG. 6 is a flow chart showing an example of the processing procedure of the image sensor 3 according to the embodiment. In FIG. 6, the image sensor 3 stores the image data 32b acquired by the imaging unit 31 (step S11) in the memory 32 and accumulates them (step S12). The stored image data 32b is passed to the space usage data generator 33a.

The space use data generator 33a performs image processing on the image data 32b under the set parameters to generate space use data (step S13). The generated space use data is transmitted from the communication unit 34 to the gateway device 7 via the sensor network 9 .

FIG. 7 is a diagram showing an example of an image within the field of view of the imaging section 31 of the image sensor 3. As shown in FIG. FIG. 7 shows a one-frame image captured at a specific point in time, and captures the location of a person at this point in time. Some are seated, some are walking. The imaging unit 31 accumulates time-series image data (frames) in the memory 32 . Image data over a period of time is then processed to generate space usage data reflecting, for example, the duration of a person's presence for each cell.

FIG. 8 is a diagram showing an example of data flow in the image sensor 3. As shown in FIG. In FIG. 8, the image data 32b acquired by the imaging unit 31 is temporarily stored in the memory 32 and then transferred to the space usage data generation unit 33a of the processor 33. FIG. The space use data generator 33a uses the memory parameter 32c to generate space use data from the image data 32b. The generated space usage data is transmitted from the communication unit 34 to the gateway device 7 .

FIG. 9 is a flow chart showing an example of the processing procedure of the gateway device 7. As shown in FIG. In FIG. 9, the gateway device 7 communicates with the image sensors 3-1 to 3-n (step S21) and acquires space usage rate data (sensing information) individually detected by the image sensors 3-1 to 3-n. and stores it in the storage unit 74 (step S22). Next, the gateway device 7 integrates the accumulated space use data 74c with respect to the common target space by the integration processing unit 75b.

The gateway device 7 applies a predetermined calculation algorithm (calculation formula) to the integrated space use data to calculate the space use rate (step S23) and stores it in the storage unit 74 (step S24).
Next, the gateway device 7 analyzes the accumulated space utilization rate 74d, calculates various trends over a predetermined period, for example (step S25), and predicts the future space utilization rate by, for example, statistical analysis. Predict (step S26).
Next, the gateway device 7 visualizes the space usage rate to create visualization data (step S27), and transmits this visualization data to the BEMS server 5, the management terminal 20, and the like (step S28).

FIG. 10 is a schematic diagram showing an example of the space usage rate. The period of stay of a person is graphically shown for each cell (square) set in the target space. In FIG. 10, for example, 60 minutes is set as the maximum value, and the sitting time is visualized with, for example, 4 ranks until the length of stay reaches 10 minutes. By visualizing the sitting time from the accumulated image data and further analyzing it, for example, a value such as ([Seating rate] from 9:00 to 10:00 on DD, MM, 2021 is 48%) is calculated. be. This [seat rate] is an example of the space usage rate.

FIG. 11 is a diagram showing an example of data flow in the gateway device 7. As shown in FIG. In FIG. 11, the communication unit 77 acquires space use data from the image sensors 3-1 to 3-n via the sensor network 9 and accumulates it. The integration processing unit 75b reads the space usage data 74c for each image sensor (area) accumulated in the storage unit 74, integrates them, and generates space usage rate data in the target space. The space usage rate calculator 75c generates a space usage rate from the space usage rate data in the target space using a predetermined algorithm. The space usage rate of the target space is passed to the analysis unit 75e to obtain various trends, and is also passed to the prediction unit 75d to calculate the future space usage rate. The visualization unit 75f visualizes the current space usage rate, the trend of the space usage rate, and the future space usage rate, and visualized data as shown in FIGS. 12 and 13 are obtained. This visualized data is passed to the communication unit 77 and transmitted to the destination.

FIG. 12 is a diagram showing an example of an image (heat map) showing the distribution, direction, density, etc. of the space usage rate in different colors. For example, by using colors such as red to indicate areas where people frequently pass by (dense space utilization), yellow for areas where few people pass by (sparse space utilization), and orange for the middle (color map), You can grasp the degree of congestion and flow lines on the floor at a glance. Of course, different colors may be used according to the length (magnitude) and direction of the space usage rate. This kind of heat map can be applied, for example, to office floors, airports or train station concourses.

By utilizing the analysis results of the space utilization rate, it is possible to grasp the current situation of offices, stores, public facilities, factories, etc., and make improvements. For example, the following two examples are typical.
(1) Examples of office improvements
Analyze the utilization rate of a conference room, etc., and change areas with low utilization rates to other uses. Estimate the occupancy rate of free-address offices, areas of people with multiple jobs, etc., and change unnecessary areas to other uses. Change the flow line to a smart layout.
(2) Examples of store improvements
Display ads in high-traffic areas. Arrange related products according to the flow line.

Another conceivable application is to grasp the congestion status of a cafeteria in almost real time and display it on the user's smartphone.
As shown in FIG. 13, if the congestion status is color-coded for each area of a predetermined space (dining room, etc.) and published as a heat map that can be accessed via the Web, it is possible to improve convenience for users.

<effect>
As described above, in this embodiment, the space usage data as the basis for calculating the space usage rate is calculated from the image data captured by the image sensors 3-1 to 3-n for each area. The image sensors 3-1 to 3-n transmit the space use data for each area to the gateway device 7 as sensing data. The gateway device 7 integrates the acquired space usage data based on the map data 74b to generate space usage data in units of the target space, and further converts the integrated space usage data into data for device control. It was made to calculate the space utilization rate as. In other words, in addition to calculating space use data such as a color map, it is possible to calculate a control amount that can be directly applied to a device to be controlled over the entire target space. Therefore, it is possible to calculate a detailed space utilization rate that cannot be obtained by a single image sensor, and thereby dramatically improve the flexibility and accuracy of air conditioning control and lighting control in a building.

Further, in the embodiment, when calculating the space utilization rate, a plurality of parameters and a plurality of calculation formulas (algorithms) are registered in the gateway device 7 in advance, and the space utilization rate under different parameters and algorithms is calculated. made it possible to calculate Since this is done, it is possible to calculate the space usage rate according to the characteristics of the target space and building. For example, by using trends in space usage rates by month, season, etc., it is possible to make use of this to improve the equipment layout of the office.

From these, according to the embodiment, it is possible to provide an image sensor, a sensing method, and a program capable of calculating the space usage rate. As a result, for example, it becomes possible to output an index indicating the degree to which a person stays for each location, and it is possible to provide an image sensor system with improved compatibility with device control.

In addition, this invention is not limited to the said embodiment. For example, the imaging unit 31 is not limited to a fish-eye lens, and it is also possible to use another type of wide-angle lens such as a so-called panorama lens using a mirror surface.

Also, the object to be detected is not limited to a person, and may be an object other than a person. For example, if you use a background dictionary for detecting and identifying the background in an office, a desk dictionary for detecting desks, a chair dictionary for detecting chairs, or a dictionary for detecting fixtures and multifunction devices, only people can be detected. Instead, it can detect various objects.
Also, various functions implemented in the gateway device 7 as a computing device may be implemented in the management terminal 20 or the BEMS server 5 .

While embodiments of the invention have been described, the embodiments are provided by way of example and are not intended to limit the scope of the invention. This novel embodiment can be embodied in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the scope of the invention described in the claims and equivalents thereof.

DESCRIPTION OF SYMBOLS 1... Lighting installation, 2... Air conditioner, 3... Image sensor, 3-1-3-n... Image sensor, 4... Lighting controller, 5... BEMS server, 7... Gateway apparatus, 8... Building network, 9... Sensor Network 10... Communication network 20... Management terminal 30... Register 30a... Camera information 31... Imaging unit 31a... Fisheye lens 31b... Aperture mechanism 31c... Image sensor 32... Memory 32a... Program 32b... Image data 32c Parameter 33 Processor 33a Space usage data generator 34 Communication unit 35 Internal bus 72 ROM 73 RAM 74 Storage unit 74a Program 74b Map data , 74c... Space usage data, 74d... Space usage rate, 74e... Algorithm, 75... Processor, 75a... Acquisition unit, 75b... Integrated processing unit, 75c... Space usage rate calculation unit, 75d... Prediction unit, 75e... Analysis unit, 75f...Visualization part, 77...Communication part, 100...Cloud, 200...Server, 300...Database, 400...Building.

Claims (10)

a plurality of image sensors installed in the target space;
a computing device in communication with the image sensor;
The image sensor is
an imaging unit that acquires image data of each assigned area;
a processor that performs image processing on the image data to generate space usage data that indicates the distribution of moving objects in the area;
The computing device
an acquisition unit that acquires the space usage data from the plurality of image sensors;
a storage unit that stores map data indicating a correspondence relationship between the target space and the area;
an integration processing unit that integrates the acquired space usage data based on the map data to generate space usage data in the target space;
an image sensor system, comprising: a space usage rate calculation unit that calculates a space usage rate, which is an index indicating the extent to which a moving body uses the target space for each location, based on space usage data in the target space. The computing device
a storage unit for accumulating the calculated space utilization rate;
2. The image sensor system of claim 1, further comprising a predictor that predicts future space utilization based on the accumulated space utilization. The computing device
a storage unit for accumulating the calculated space utilization rate;
2. The image sensor system of claim 1, further comprising an analysis unit that analyzes trends regarding the space utilization based on the accumulated space utilization. The computing device
2. The image sensor system of claim 1, further comprising a visualization unit that visualizes the calculated space utilization. The computing device
a storage unit for accumulating the calculated space utilization rate;
a prediction unit that predicts future space utilization based on the accumulated space utilization;
2. The image sensor system of claim 1, further comprising a visualization unit that visualizes the future space utilization. The computing device
a storage unit for accumulating the calculated space utilization rate;
an analysis unit that analyzes a trend regarding the space utilization rate based on the accumulated space utilization rate;
2. The image sensor system of claim 1, further comprising a visualization unit that visualizes trends regarding the space utilization. 2. The image sensor system according to claim 1, wherein said processor senses at least one of presence/absence, number of people, amount of activity, illuminance, and staying on foot as sensing items in said area. A computing device in communication with a plurality of image sensors each generating space usage data in an assigned area, comprising:
an acquisition unit that acquires the space usage data from the plurality of image sensors;
a storage unit that stores map data indicating the correspondence relationship between the target space and the areas assigned to each of the image sensors;
an integration processing unit that integrates the acquired space usage data based on the map data to generate space usage data in the target space;
an image sensor system, comprising: a space usage rate calculation unit that calculates a space usage rate, which is an index indicating the extent to which a moving body uses the target space for each location, based on space usage data in the target space. A computer implemented sensing method comprising:
the computer obtaining the space usage data from a plurality of image sensors each generating space usage data in an assigned area;
The computer generates space usage data in the target space by integrating the acquired space usage data based on map data indicating the correspondence relationship between the target space and the allocated areas for each of the image sensors. ,
A sensing method, wherein the computer calculates a space usage rate, which is an index indicating the extent to which a moving body uses the target space for each location, based on space usage data in the target space. a computer capable of communicating with a plurality of image sensors each generating spatial usage data in an assigned area;
obtaining the space usage data from a plurality of image sensors each generating space usage data in an assigned area;
generating space usage data in the target space by integrating the acquired space usage data based on map data indicating the correspondence relationship between the target space and the allocated areas for each of the image sensors;
calculating a space usage rate, which is an index indicating the extent to which a moving body uses the target space for each location, based on space usage data in the target space.

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