JP2018195292A - Information processing apparatus, information processing system, information processing method, and program - Google Patents

Abstract

To accurately determine the state of a matrix.SOLUTION: An information processing apparatus comprises: detection means that detects objects in determination areas of an image; determination means that determines whether the determination areas are in a congestion state on the basis of the number of objects in the determination areas detected by the detection means; and judging means that, when the first determination area is determined by the determination means to be in a congestion state and the second determination area adjacent to the first determination area is determined to be in a congestion state, determines the second determination area to be in a matrix state.SELECTED DRAWING: Figure 6

Description

  The present invention relates to an information processing apparatus, an information processing system, an information processing method, and a program.

  User queues are generated at airport security checkpoints, station ticketing counters, bank automated teller machines, and store payment sites. Patent Document 1 discloses a technique for detecting a queue from an image of a monitoring camera based on a background difference and estimating the number of queued people.

  FIG. 1 is a diagram for explaining a matrix analysis method. In the information processing system of FIG. 1A, a camera 102 and a PC 103 are connected to a network 104 so that they can communicate with each other. The camera 102 observes the real area 120 such as the ticket gate 101 in the field of view. In the real area 120, a plurality of persons including persons 111, 112, and 113 are arranged in a matrix, and the matrix moves from left to right. FIG. 1B shows a matrix length determination method based on the background difference. A region 220 is a region on the image corresponding to the real region 120. The PC 103 applies the background difference to the area 220. Then, the difference result is detected as a plurality of partial areas 221, 222, and 223. Next, the PC 103 connects the partial areas. As a result, the left end of the partial region 223 is determined as the end of the matrix. The PC 103 connects the partial areas based on, for example, the shortest distance between the area boundaries. In order to remove noise, the PC 103 selects a region having an area equal to or larger than a predetermined value as the partial regions 221, 222, and 223 that are candidates for connection.

JP 2005-216217 A

  The background difference method is vulnerable to lighting conditions and background fluctuations, and it is not easy to separate a person and a luggage under crowded conditions. As a result, there has been a problem that the matrix cannot be accurately determined.

  The information processing apparatus according to the present invention determines a detection unit that detects an object in a determination region of an image and whether the determination region is in a congested state based on the number of objects in the determination region detected by the detection unit. And when the first determination area is determined to be congested by the determination means and the second determination area adjacent to the first determination area is determined to be congested, the second determination area Determination means for determining the determination region as a matrix state.

  According to the present invention, it is possible to accurately determine a matrix.

It is a figure explaining a matrix analysis system. It is a figure which shows an example of the system configuration | structure of an information processing system. FIG. 6 is a diagram (part 1) for explaining region definition; It is a figure which shows an example of the hardware constitutions of PC. It is a figure which shows an example of the software configuration of PC. 3 is a flowchart illustrating an example of information processing according to the first exemplary embodiment. It is FIG. (2) explaining area | region definition. 10 is a flowchart illustrating an example of information processing according to the second exemplary embodiment. It is a figure which shows an example of the hardware constitutions of a camera. It is a figure which shows an example of the software configuration of an information processing system. 10 is a flowchart illustrating an example of information processing according to a third exemplary embodiment.

<Embodiment 1>
In the present embodiment, the PC 103 divides an area where a matrix is formed into a plurality of areas and designates them in advance. Then, the PC 103 determines whether the matrix has been extended to a specific area by counting the number of people detected in the designated area.
FIG. 2 is a diagram illustrating an example of a system configuration of the information processing system. The system configuration of the information processing system in FIG. 2 is the same as that of the information processing system in FIG. The area where the matrix is formed is divided into a plurality of areas (real areas 131, 132, 133), and the PC 103 detects a person in each area. In this embodiment, human body detection technology is used for human detection, and 121, 122, and 123 are human body detection results. The PC 103 counts the number of people in each area, and determines whether the matrix has extended to a specific area based on the result. In the example of FIG. 2, the direction toward the real areas 131, 132, and 133 is the direction in which the column extends.

  FIG. 3 is a diagram for explaining region definition. The matrix area 300 is an area defined on the image of the camera 102 and includes a plurality of partial areas 310, 320, and 330. The partial areas 310, 320, and 330 correspond to the real areas 131, 132, and 133. Since the result of counting the number of people in the partial area is used for determination, these areas are hereinafter referred to as determination areas. The determination area further corresponds to a detection area set on the image. The detection area has camera connection information and polygonal area coordinate information as set values. In the example of FIG. 3, the determination area 310 corresponds to the detection area 311. Similarly, the determination area 320 corresponds to the detection area 321, and the determination area 330 corresponds to the detection area 331. The PC 103 defines a matrix area in the image by a set of a plurality of determination areas, and detects that the matrix has extended to a specific determination area by using the number of people in the determination area.

FIG. 4 is a diagram illustrating an example of a hardware configuration of the PC 103. The PC 103 includes various units (10 to 16). A CPU (Central Processing Unit) 10 is a unit that executes various programs and realizes various functions. A RAM (Random Access Memory) 11 is a unit that stores various types of information. The RAM 11 is a unit that is also used as a temporary work storage area of the CPU 10. A ROM (Read Only Memory) 12 is a unit that stores various programs. For example, the CPU 10 loads a program stored in the ROM 12 into the RAM 11 and executes the program.
In addition, the CPU 10 executes processing based on a program stored in the external storage device 13 such as a flash memory, an HDD (Hard Disk Drive), or an SSD (Solid State Disk). Thereby, the software configuration of the PC 103 as shown in FIGS. 5 and 10B and the processing of each step of the flowcharts of FIGS. 6, 8, and 11B described later are realized.
Dedicated hardware is used for the software configuration of the PC 103 as shown in FIGS. 5 and 10B and the processing of each step in the flowcharts of FIGS. 6, 8, and 11B. It may be realized.
A NIC (Network Interface Card) 205 is a unit for connecting the PC 103 to a network.
The input device I / F 15 is an interface that connects the PC 103 and an input device 130 such as a mouse and a keyboard.
The display device I / F 16 is an interface that connects the PC 103 and a display device 140 such as a display.
The PC 103 may be realized by one device or a computer as shown in FIG. 4 or may be realized by a plurality of devices or computers.

FIG. 5 is a diagram illustrating an example of a software configuration of the PC 103.
The PC 103 includes a communication unit 401, an image acquisition unit 402, a human body detection unit 403, an area number counting unit 404, a matrix state determination unit 405, a result output unit 406, and an area setting unit 407 as software configurations.
The image acquisition unit 402 acquires an image captured by the camera 102. The acquired image does not depend on a specific image format or compression method.
The human body detection unit 403 detects a person position and orientation in the acquired image. The detection process by the human body detection unit 403 does not depend on a specific person detection method. For example, the human body detection unit 403 detects a person using a method of detecting a silhouette shape of the upper body, a method of detecting a face, a method of detecting a person by learning other feature amounts, and the like.
The area number counting unit 404 detects the number of persons on the determination areas 310, 320, and 330 using a human body detection parameter or the like, and counts based on the detection result. The area setting unit 407 sets a determination area as illustrated in FIG. 3 according to an input operation or the like via the input device 410.
The matrix state determination unit 405 determines whether or not each determination region is congested based on the result of counting the number of people for each determination region, and determines whether the adjacent determination region is congested or not. It is determined whether or not the determination area is in a matrix state.
The result output unit 406 outputs the determination result of the matrix state determination unit 405. The result output unit 406 outputs the determination result to a file, displays it, or sends it to another device.

FIG. 6 is a flowchart illustrating an example of information processing. FIG. 6A shows the main processing, and FIG. 6B shows details of the processing in S507.
In step S501, the image acquisition unit 402 acquires an image.
In step S <b> 502, the human body detection unit 403 detects a human body from the processing target determination area of the acquired image.
In step S504, the area number counting unit 404 counts the number of human bodies detected in the determination area to be processed.
In S505, the matrix state determination unit 405 determines the congestion state of the determination region. For example, the CPU 10 sets a threshold for the number of people in advance for each determination area in accordance with an input operation via the input device 130 or the like, and sets it in a file or the like in the RAM 11, ROM 12, external storage device 13, or the like. The matrix state determination unit 405 acquires the threshold value of the number of persons for the determination area to be processed from a file or the like, and when the number of human bodies detected from the determination area to be processed reaches the acquired threshold value, the determination area for the processing object Is determined to be congested. The process of S505 is an example of a determination process.
In step S <b> 506, the matrix state determination unit 405 determines whether the processing for all the determination regions has been completed. If the matrix state determination unit 405 determines that the processing for all the determination regions has been completed (YES in S506), the process proceeds to S507, and if it is determined that the processing for all the determination regions has not been completed (NO in S506), S504 Return processing to.
In S507, the matrix state determination unit 405 determines whether or not the specific determination region is in the matrix state based on the determination region determined to be in the congested state. For example, the matrix state determination unit 405 determines whether or not a specific determination region is in a matrix state based on whether or not the matrix extends from a predetermined adjacent determination region. Details of the processing of S507 are shown in FIG.
In S508, the result output unit 406 outputs a result based on the determination result in S507. The result output unit 406 outputs the result to a file, displays it, and sends it to another device.
In step S510, the result output unit 406 determines whether to end the process of the flowchart illustrated in FIG. If the result output unit 406 determines to end the process (YES in S510), it ends the process of the flowchart shown in FIG. 6A, and if it determines not to end the process (NO in S510), returns the process to S501. .

Details of S507 are shown from S520 to S525 in FIG. In step S507, it is determined whether or not the specific determination region is in a matrix state. The area to be determined is N.
In S520, the matrix state determination unit 405 sets the matrix state flag to true.
In S521, the matrix state determination unit 405 sets the region variable R of the determination region to 1.
In step S522, the matrix state determination unit 405 determines whether the region indicated by R is congested based on the determination result in step S505. If the area indicated by R is not congested (NO in S522), the matrix state determination unit 405 proceeds to S523, and if the area indicated by R is congested (YES in S522), the process advances to S524.
In S523, the matrix state determination unit 405 sets the matrix state flag to false.
In S524, the matrix state determination unit 405 increases (increments) the value of R by one.
In step S525, the matrix state determination unit 405 determines whether the determination up to the region N has been completed. If the matrix state determination unit 405 determines that the determination up to the region N has been completed (YES in S525), the process of the flowchart illustrated in FIG. 6B ends, and it is determined that the determination up to the region N has not been completed. Then (NO in S525), the process returns to S522.
It can be determined whether or not the region N is in a matrix state by the processing of FIG.
The processing from S520 to S525 can be simplified when N is several. For example, when N = 3, implementation by a logical expression such as “region 3 is in a matrix state when region 1, region 2, and region 3 are all congested” is possible.
Further, in the determination of the congestion state in S505, the matrix state determination unit 405 determines a threshold value for determining the number of people to reach the congestion state based on the statistic of the number of detected human bodies in the determination region in the most recent period. It may be determined. For example, the matrix state determination unit 405 may determine the threshold using a value obtained by multiplying the average value and the mode value of the number of detected human bodies in the determination region in the most recent period by a predetermined constant.

  According to the above method, the CPU 10 arranges the determination areas from the top to the end of the matrix, and determines the congestion state of the matrix using the person detection result. Next, the CPU 10 determines the matrix state of the determination region based on the congestion state of the matrix. Thereby, the CPU 10 can make a determination based on an accurate number of people. As a result, the CPU 10 can efficiently take measures such as increasing the number of staff for guidance when the procession extends to a predetermined position.

<Modification 1>
As a modification of the above-described embodiment, the PC 103 has a human body tracking function in addition to the human body detection function, and detects the moving speed of the human body in addition to the position of the human body. A person who simply passes through the determination area is also detected, but such a person is expected to have a higher moving speed than the persons lined up in the matrix. Therefore, in each determination area, the PC 103 excludes the human body whose moving speed is equal to or higher than a certain value from the target of counting the number of persons (determining whether the moving state is congested based on the number of objects whose moving speed is less than the predetermined value). Become). As a process, the PC 103 adds a human body tracking process for excluding a human body whose moving speed is equal to or greater than a certain value after S502 in FIG. As described above, it is possible to improve the discrimination accuracy as a result of detecting a person who is truly waiting in a queue and using it to discriminate the queue state.

<Modification 2>
As a modification of the above-described embodiment, a matrix determination region may be defined using a plurality of cameras. The area setting unit 407 sets a determination area as shown in FIG. 7 according to an input operation or the like via the input device 410. This is effective when the shape of the matrix is complicated. For example, in FIG. 7, the determination area (2) 320 includes a detection area 2 (322) of the camera 1 and a detection area 1 (323) of the camera 2. The detection area 2 (322) of the camera 1 is a partial area of the image acquired from the camera 1, and the detection area 1 (323) of the camera 2 is a partial area of the image acquired from the camera 2. The number of people counted in the determination region (2) 320 is the sum of the number of people counted in the detection region 2 (322) and the detection region 1 (323). Similarly, the determination area (3) 330 includes a detection area 2 (332) of the camera 2 and a detection area 3 (333) of the camera 3. The detection area 2 (332) of the camera 2 is a partial area of the image acquired from the camera 2. The detection area 3 (333) of the camera 3 is a partial area of the image acquired from the camera 3. The number of people counted in the determination area (3) 330 is the sum of the count numbers of the detection area 3 (333) and the detection area 2 (332).
If a plurality of cameras are used, it is possible to cope with a case where a certain determination region cannot be accommodated in the field of view of one camera, a case where the shape of the matrix is complicated and hiding occurs. It is also effective when the matrix extends in the depth direction and there is a case where a person cannot be detected depending on the position in the screen.

<Embodiment 2>
As the second embodiment, the matrix state determination unit 405 can also determine the end of the matrix based on the determination of the matrix state. For example, the matrix state determination unit 405 confirms whether the determination region R is continuously congested in order from the top, and sets the region immediately before the region that is no longer congested as the end region.
In the process of this embodiment, the matrix state determination process (S520 to S525) in S507 of FIG. 6 is replaced as shown in FIG. R is a variable indicating the determination region.
In step S701, the matrix state determination unit 405 initializes R to 0.
In S702, the matrix state determination unit 405 determines whether or not the determination region R + 1 is congested. If the determination region R + 1 is in a congested state (YES in S702), the matrix state determination unit 405 proceeds to S703, and if the determination region R + 1 is not in a congested state (NO in S702), the process proceeds to S705.
In step S703, the matrix state determination unit 405 increments the value of R by one.
In step S704, the matrix state determination unit 405 determines whether the processing has been completed for all determination regions. When the process is completed for all the determination areas, that is, when R = N (YES in S704), the matrix state determination unit 405 proceeds to S705 and has not completed the process for all the determination areas. If so (NO in S704), the process returns to S702.
In S705, the matrix state determination unit 405 sets the determination region R as the end.

  As a result of the above processing, when the end is the determination area 0, the CPU 10 knows that there is no matrix. When the end is N, which is the same as the number of determination areas, the CPU 10 knows that the matrix extends to the end of the predetermined determination area group, and the true end is in addition to the set determination area. As a result of the determination, the CPU 10 can output information on the matrix (for example, information on the determination area at the end) or the like to the display device 140 or the like.

<Embodiment 3>
In the above-described embodiment, the PC 103 has each function and performs processing. In the third embodiment, an example will be described in which the PC 103 and the camera 102 perform processing and the like performed by the PC 103 in the above-described embodiment.
FIG. 9 is a diagram illustrating an example of a hardware configuration of the camera 102. The camera 102 includes various units (20 to 26). The imaging unit 20 includes an optical system and a control device, and is a unit that captures an image by controlling the optical system according to a scene. The CPU 21 is a unit that executes various programs and realizes various functions. The RAM 22 is a unit that stores various types of information. The RAM 22 is a unit that is also used as a temporary work storage area for the CPU 21. The ROM 23 is a unit that stores various programs. For example, the CPU 21 loads a program stored in the ROM 23 into the RAM 22 and executes the program. Thereby, the software configuration of the camera 102 as shown in FIG. 10 and the processing of each step in the flowchart of FIG. The software configuration of the camera 102 and all or some of the steps in the flowchart of FIG. 11A described later may be realized using dedicated hardware.
The communication unit 24 is a unit for connecting the camera 102 to a network. The input unit 25 is a unit for inputting operation information to the CPU 21 based on a user operation. The display unit 26 is a unit for displaying the result of processing by the CPU 21.

FIG. 10A is a diagram illustrating an example of a software configuration of the camera 102.
The camera 102 includes an image acquisition unit 501, a human body detection unit 502, an area number counting unit 503, an area setting unit 504, and a communication unit 505 as software configurations.
The image acquisition unit 501 acquires the image captured by the imaging unit 20 and converts it into a format that can be processed.
The human body detection unit 502 detects a person position and orientation in the acquired image. The detection process by the human body detection unit 502 does not depend on a specific person detection method. For example, the human body detection unit 502 detects a person using a method of detecting a silhouette shape of the upper body, a method of detecting a face, a method of detecting a person by learning other feature amounts, and the like.
The area number counting unit 503 counts the number of persons on the determination area. For example, the area number counting unit 503 detects the number of persons on the determination areas 310, 320, and 330 in FIG. 3 using a human body detection parameter or the like, and counts based on the detection result.
The communication unit 505 transmits the result counted by the area number counting unit 503 to the PC 103 via the network. The communication unit 505 receives information from the PC 103 via the network.
The area setting unit 504 sets a determination area as shown in FIG. 3 according to the input operation via the input unit 25 or the like, or the area setting setting information received from the PC 103 via the communication unit 505. The region setting unit 504 can set a plurality of regions on the same image, or can set a plurality of regions on the plurality of images when the matrix does not fit in the field of view of one camera.

FIG. 10B is a diagram illustrating an example of a software configuration of the PC 103.
The PC 103 includes a communication unit 506, a matrix state determination unit 507, a result output unit 508, and an area setting unit 509 as software configurations.
The communication unit 506 receives the count result of the number of persons on the determination area from the camera 102 via the network. The communication unit 506 transmits the setting information to the camera 102 via the network when the region setting setting information is set in the region setting unit 509.
The matrix state determination unit 507 determines whether or not each determination region is congested based on the result of counting the number of people for each determination region and the parameters set by the region setting unit 509, and the adjacent determination region is congested. Based on whether or not there is, it is determined whether or not the determination region to be processed is in a matrix state.
The area setting unit 509 sets parameters related to determination of a matrix state set by a user operation via a GUI (graphical user interface) on the display device 140 and the input device 130. The area setting unit 509 sets area setting setting information set by a user operation via the GUI on the display device 140 and the input device 130.
The result output unit 508 outputs the determination result of the matrix state determination unit 507. The result output unit 508 outputs the determination result as a file to the external storage device 13, displays it on the display device 140, and transmits it to another device or the like.
In the present embodiment, communication between the camera 102 and the PC 103 does not depend on a specific communication method or protocol. For example, the protocol may be HTTP (HyperText Transfer Protocol), RTP (Real-time Transport Protocol), and ONVIF (Open Network Video Interface).

FIG. 11 is a flowchart illustrating an example of information processing according to the third embodiment. FIG. 11A is a flowchart illustrating an example of information processing on the camera 102 side.
In step S1001, the image acquisition unit 501 acquires an image.
In step S <b> 1002, the human body detection unit 502 detects a human body from the determination area to be processed of the acquired image.
In step S <b> 1004, the area number counting unit 503 counts the number of human bodies detected in the determination area to be processed.
In step S <b> 1006, the area number counting unit 503 determines whether the counting process for all the determination areas is finished. If the area number counting unit 503 determines that the counting process for all the determination areas has been completed (YES in S1006), the process proceeds to S1008 and determines that the counting process for all the determination areas has not been completed (in S1006). NO), the process is returned to S1004.
In step S1008, the communication unit 505 transmits the result counted by the area number counting unit 503 to the PC 103 via the network.
In step S1010, the communication unit 505 determines whether to end the process of the flowchart illustrated in FIG. If communication unit 505 determines to end the process (YES in S1010), it ends the process of the flowchart shown in FIG. 11A, and determines that the process is not ended (NO in S1010), returns the process to S1001.

FIG. 11B is a flowchart illustrating an example of information processing on the PC 103 side.
In step S <b> 1020, the communication unit 506 receives the human body detection count results in all determination areas from the camera 102 (a plurality of cameras when the information processing system includes a plurality of cameras).
In S1021, the matrix state determination unit 507 sets the matrix state flag to true.
In step S1022, the matrix state determination unit 507 sets the region variable R of the determination region to 1.
In step S1023, the matrix state determination unit 507 determines the congestion state of the determination region based on the count result. For example, the CPU 10 sets a threshold for the number of people in advance for each determination area in accordance with an input operation via the input device 130 or the like, and sets it in a file or the like in the RAM 11, ROM 12, external storage device 13, or the like. The matrix state determination unit 507 acquires the threshold value of the number of persons for the determination area to be processed from a file or the like, and when the number of human bodies detected from the determination area to be processed reaches the acquired threshold value, the determination area for the processing object Is determined to be congested. The process of S1023 is an example of a determination process.
In step S1024, the matrix state determination unit 507 determines whether the region indicated by R is in a congested state based on the determination result in step S1023. If the area indicated by R is not congested (NO in S1024), the matrix state determination unit 507 proceeds to S1026. If the area indicated by R is congested (YES in S1024), the process advances to S1025.
In step S1025, the matrix state determination unit 507 sets the matrix state flag to false.
In S1026, the matrix state determination unit 507 increments the value of R by one.
In step S1027, the matrix state determination unit 507 determines whether the determination up to the region N is completed. If the matrix state determination unit 507 determines that the determination up to the region N has been completed (YES in S1027), the process proceeds to S1028, and if it is determined that the determination up to the region N has not been completed (NO in S1027), S1023 Return to the process.
In step S1028, the result output unit 508 outputs the determination result. The result output unit 508 outputs the determination result as a file to the external storage device 13, displays it on the display device 140, and transmits it to another device or the like.
In step S1029, the result output unit 508 determines whether to end the process of the flowchart illustrated in FIG. If the result output unit 508 determines to end the process (YES in S1029), it ends the process of the flowchart shown in FIG. 11B, and if it determines not to end the process (NO in S1029), returns the process to S1020. .

  According to this embodiment, the number of people in the queue is counted by at least one camera, and the count result is transmitted to the PC 103. Then, the PC 103 determines the congestion state. As a result, the load on the PC can be reduced in a configuration in which a plurality of cameras are required for observing the queued number of people.

<Other embodiments>
The present invention supplies a program that realizes one or more functions of the above-described embodiments to a system or apparatus via a network or a storage medium. It can also be realized by a process in which one or more processors in the computer of the system or apparatus read and execute the program. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

The exemplary embodiment of the present invention has been described in detail above, but the present invention is not limited to the specific embodiment.
For example, a part or all of the software configuration of the PC 103 illustrated in FIGS. 5 and 10B may be mounted on the PC 103 as a hardware configuration. The hardware configuration of the PC 103 does not have to be one CPU, ROM, RAM, external storage device, NIC, or the like. A plurality of CPUs may execute processing based on a program using a plurality of RAMs, ROMs, data stored in an external storage device, and the like. Moreover, it is good also as using GPU (Graphics Processing Unit) instead of CPU.
Further, a part or all of the software configuration of the camera 102 illustrated in FIG. 10A may be implemented in the camera 102 as a hardware configuration. Further, the hardware configuration of the camera 102 does not have to be one CPU, RAM, ROM, communication unit, or the like. A plurality of CPUs may execute processing based on a program using data stored in a plurality of RAMs and ROMs. Moreover, it is good also as using GPU instead of CPU.
In each of the above-described embodiments, the case where the human body forms a matrix has been described. However, other objects such as a car and an animal may be used.

  As mentioned above, according to each embodiment mentioned above, the determination of a matrix can be performed accurately.

10 CPU
102 Camera 103 PC

Claims (12)

Detection means for detecting an object in the determination region of the image;
Determining means for determining whether or not the determination area is congested based on the number of objects in the determination area detected by the detection means;
When the determination unit determines that the first determination region is congested and the second determination region adjacent to the first determination region is determined to be congested, the second determination region is set as a matrix state. Determining means for determining
An information processing apparatus. And further comprising an acquisition means for acquiring an image captured by at least one camera,
The detection means detects an object in a determination region of the image acquired by the acquisition means,
The information processing apparatus according to claim 1, wherein the determination unit determines whether or not the determination region is congested based on the number of objects in the determination region detected by the detection unit. The determination area includes at least one detection area,
The information processing apparatus according to claim 1, wherein the detection area is a partial area of an image captured by at least one camera.   The information processing apparatus according to claim 1, further comprising a setting unit that sets the determination area.   The information processing apparatus according to any one of claims 1 to 4, wherein the determination unit determines that the object is congested when the number of objects in the determination area reaches a set threshold value.   The information processing apparatus according to claim 5, wherein the determination unit sets the threshold based on a statistic of the number of objects in the determination area.   7. The information processing according to claim 1, wherein the determination unit determines whether or not the determination region is in a congested state based on the number of objects whose moving speed is less than a certain value among the objects in the determination region. apparatus.   The determination means determines that the first determination area is congested, and the second determination area adjacent to the first determination area is determined to be congested and is adjacent to the second determination area. The information processing apparatus according to any one of claims 1 to 7, wherein when it is determined that the third determination region is not congested, the determination unit determines the second determination region as the end region of the matrix. Detection means for detecting an object in the determination region of the image;
Determining means for determining whether or not the determination area is congested based on the number of objects in the determination area detected by the detection means;
When the determination unit determines that the first determination region is congested and the second determination region adjacent to the first determination region is determined to be congested, the second determination region is set as a matrix state. Determining means for determining
An information processing system having An information processing system including at least one imaging device and an information processing device,
The imaging device
Detection means for detecting an object in the determination region of the image;
Transmitting means for transmitting the number of objects in the determination area detected by the detecting means to the information processing apparatus;
Have
The information processing apparatus includes:
Receiving means for receiving the number of objects in the determination area from the imaging device;
Determining means for determining whether the determination area is congested based on the number of objects in the determination area received by the receiving means;
When the determination unit determines that the first determination region is congested and the second determination region adjacent to the first determination region is determined to be congested, the second determination region is set as a matrix state. Determining means for determining
An information processing system having An information processing method executed by an information processing apparatus,
A detection step of detecting an object in the determination region of the image;
A determination step of determining whether or not the determination region is congested based on the number of objects in the determination region detected by the detection step;
When the determination step determines that the first determination region is congested and the second determination region adjacent to the first determination region is determined to be congested, the second determination region is in a matrix state A determination step for determining
An information processing method including:   The program for functioning a computer as each means of the information processing apparatus in any one of Claims 1 thru | or 8.

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