Information processing apparatus and specifying method

Abstract

The information processing apparatus (100) includes an acquisition unit (120) that acquires an image including a person near the door, a detection unit (130) that detects a flow of people based on the image, detects the position of the door based on the position where the flow of people occurs, detects the generation range of people as the width of the door based on the image, detects the lower edge portion of the door based on one image among a plurality of images constituting the image and the position of the door, and detects the stride of a person using one image among a plurality of images constituting the image, and a specifying unit (140) that specifies a door closing determination area, which is an area for determining whether to close the door based on the width of the door and the stride with reference to the lower edge portion of the door.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus and a specifying method.

Background Art

[0002] Doors exist in public transportation, buildings, etc. Technologies for controlling doors have been proposed (see Patent Document 1). In Patent Document 1, a first detection area and a second detection area are set. When the control system of Patent Document 1 extracts the movement of a passenger going from the first detection area to the second detection area while closing the door, it stops the door or reverses and opens the door.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As described above, in the above technology, a first and a second detection area are set. Here, an area for determining door control such as the first and second detection areas is called a door closing determination area. The door closing determination area is specified by the user. The user performs an operation of specifying the door closing determination area. Therefore, the burden on the user is large.

[0005] An object of the present disclosure is to reduce the burden on the user.

Means for Solving the Problems

[0006] An information processing apparatus according to an aspect of the present disclosure is provided. The information processing apparatus includes an acquisition unit that acquires an image including a person near a door, a detection unit that detects a flow of people based on the image, detects the position of the door based on the position where the flow of people occurs, detects the generation range of people as the width of the door based on the image, detects the lower edge of the door based on one image among a plurality of images constituting the image and the position of the door, and detects the stride of a person using one image among a plurality of images constituting the image, and a specifying unit that specifies a door closing determination area which is an area for determining whether to close the door based on the width of the door and the stride with reference to the lower edge of the door.

Effects of the Invention

[0007] According to the present disclosure, the burden on the user can be reduced.

Brief Description of the Drawings

[0008]

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Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments will be described with reference to the drawings. The following embodiments are merely examples, and various modifications are possible within the scope of the present disclosure.

[0010] Embodiment. FIG. 1 is a diagram showing a control system. The control system includes an information processing apparatus 100, a camera 200, a door driving apparatus 300, and a door 400. The information processing apparatus 100, the camera 200, the door driving apparatus 300, and the door 400 are connected via a network. The network is a wired network or a wireless network.

[0011] The information processing apparatus 100 is a device that executes a specific method. The information processing apparatus 100 can acquire video from the camera 200. The door driving apparatus 300 drives the door 400. For example, the door 400 is a train door, a station platform door, or a door existing in a building. In the following description, the door 400 is a train door.

[0012] Next, the hardware of the information processing apparatus 100 will be described. FIG. 2 is a diagram showing the hardware of the information processing apparatus. The information processing apparatus 100 has a processor 101, a volatile memory device 102, a non-volatile memory device 103, and a communication interface 104.

[0013] The processor 101 controls the entire information processing apparatus 100. For example, the processor 101 is a CPU (Central Processing Unit), an FPGA (Field Programmable Gate Array), or the like. The processor 101 may be a multi-processor. Further, the information processing apparatus 100 may have a processing circuit.

[0014] The volatile memory device 102 is the main memory device of the information processing device 100. For example, the volatile memory device 102 is a RAM (Random Access Memory). The non-volatile memory device 103 is the auxiliary memory device of the information processing device 100. For example, the non-volatile memory device 103 is an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The communication interface 104 communicates with the camera 200 and the door drive device 300.

[0015] Next, the functions of the information processing device 100 will be described. FIG. 3 is a block diagram showing the functions of the information processing device. The information processing device 100 includes a storage unit 110, an acquisition unit 120, a detection unit 130, an identification unit 140, and a determination unit 150.

[0016] The storage unit 110 may be realized as a storage area secured in the volatile memory device 102 or the non-volatile memory device 103. Some or all of the acquisition unit 120, the detection unit 130, the identification unit 140, and the determination unit 150 may be realized by a processing circuit. Also, some or all of the acquisition unit 120, the detection unit 130, the identification unit 140, and the determination unit 150 may be realized as modules of a program executed by the processor 101.

[0017] The storage unit 110 stores various information. The acquisition unit 120 acquires video. For example, the acquisition unit 120 acquires video from the camera 200. The acquisition unit 120 may acquire video via another device. The video is video including people near the door 400.

[0018] The detection unit 130 detects the flow of people based on the video. The detection unit 130 may detect the flow of people based on motion vectors such as optical flow. Also, the detection unit 130 may detect the flow of people using image processing techniques such as background subtraction.

[0019] The detection unit 130 detects the position of the door 400 based on the position where the flow of people occurs. The detection unit 130 may periodically detect the position of the door 400 (hereinafter referred to as the door position) based on the position where the flow of people occurs. The detection process will be described using a specific example.

[0020] FIG. 4 is a diagram showing a specific example of the detection process of the door position. FIG. 4 shows an image 10. The image 10 is one of the plurality of images constituting the video. The image 10 shows a train 11, a person 12, and a visually impaired person guiding block 13 (that is, a yellow braille block). Also, FIG. 4 shows the flow of people for ease of explanation. The detection unit 130 detects the door position 14 based on the position where the flow of people occurs.

[0021] The detection unit 130 detects the range where people occur as the width of the door 400 based on the video. The specific detection process will be described. The detection unit 130 detects people based on the video. Specifically, the detection unit 130 uses image recognition technology to detect people. For example, the detection unit 130 uses the video and a learned model to detect people. When a learned model is used, the learned model is acquired by the acquisition unit 120 from the storage unit 110 or an external device. The external device is a device existing outside the information processing device 100. For example, the external device is a cloud server, an external memory, etc. The figure of the external device is omitted. By detecting people, the detection unit 130 can detect the range where people occur. Then, the detection unit 130 detects the range where people occur as the width of the door 400 (hereinafter referred to as the door width). The detection process will be described using a specific example.

[0022] FIG. 5 is a diagram showing a specific example of the detection process of the door width. FIG. 5 shows an image 20. The image 20 is one of the plurality of images constituting the video. The detection unit 130 uses the image 20 and a learned model to detect people. As a result, people are detected. For example, the detection of people is represented by bounding boxes 21, 22.

[0023] The detection unit 130 can detect the occurrence range of a person by repeatedly detecting a person using a video (i.e., a plurality of images). For example, FIG. 4 shows the occurrence range of a person with double-headed arrow 23. The range indicated by double-headed arrow 23 is the door width.

[0024] The detection unit 130 detects the lower edge portion of the door 400 based on one image among the plurality of images constituting the video and the position of the door 400. Also, the detection unit 130 detects the stride of a person using one image among the plurality of images constituting the video. This image may be the same as the image used when detecting the lower edge portion of the door 400. In the following description, it is assumed that this image is the same as the image used when detecting the lower edge portion of the door 400. The detection processes of the lower edge portion of the door 400 and the stride will be described using specific examples.

[0025] FIG. 6 is a diagram showing a specific example of the detection process of the lower edge portion of the door and the stride. FIG. 6 shows an image 30. The image 30 is one image among the plurality of images constituting the video. The image 30 includes persons 31, 32. Also, FIG. 6 shows a door position 33 for ease of explanation.

[0026] The detection unit 130 detects the lower edge portion of the door 400 based on the image 30 and the door position 33. Specifically, the detection unit 130 detects the point where the line 34 extended from the feet of the person 31 in the direction of the door position 33 intersects the door position 33 as the lower edge portion 35 of the door 400.

[0027] The detection unit 130 detects the stride of the person 32 based on the image 30. The stride detection process will be described. For example, the detection unit 130 extracts the skeletal information of the person 32 using the image 30 and the learned model. Specifically, the detection unit 130 extracts the skeletal information of the person 32 using OpenPose. The detection unit 130 detects the stride 36 of the person 32 based on the skeletal information of the person 32.

[0028] The detection unit 130 may detect a plurality of step widths based on one or more images constituting the video, and detect the average value or representative value of the plurality of step widths as the step width of a person. By setting the average value or representative value as the step width of a person, the accuracy of the step width of a person is improved.

[0029] The specifying unit 140 specifies a door closing determination region based on the width of the door 400 and the step width with reference to the lower side portion of the door 400. Note that the door closing determination region is a region for determining whether to close the door 400. Specifically, the specifying unit 140 specifies the door closing determination region from the image. The specifying process will be described using a specific example.

[0030] FIG. 7 is a diagram showing a specific example of the process for specifying the door closing determination region. FIG. 7 shows an image 40. The image 40 is one of the plurality of images constituting the video. FIG. 7 shows the lower side portion 41 of the door 400, the width 42 of the door 400, and the step width 43. The specifying unit 140 specifies the door closing determination region 44 based on the width 42 of the door 400 and the step width 43 with reference to the lower side portion 41 of the door 400. The door closing determination region 44 can be expressed by Equation (1).

[0031] Door closing determination region = Door width × Step width ···(1)

[0032] The specifying unit 140 may specify the door closing determination region as follows. When specifying the door closing determination region, the specifying unit 140 adds a constant to at least one of the width of the door 400 and the step width to specify the door closing determination region. Specifically, the door closing determination region can be expressed by any one of Equations (2) to (4).

[0033] Door closing determination region = (Constant × Door width) × Step width ···(2)

[0034] Door closing determination region = Door width × (Constant × Step width) ···(3)

[0035] Door closing determination region = (Constant × Door width) × (Constant × Step width) ···(4)

[0036] Incidentally, for example, the constant is "2". In this way, the information processing apparatus 100 can add a constant to give some leeway in the determination when closing the door 400.

[0037] Next, the processing executed by the information processing apparatus 100 will be described using a flowchart. FIG. 8 is a flowchart showing an example of the processing executed by the information processing apparatus. (Step S11) The acquisition unit 120 acquires an image. (Step S12) The detection unit 130 detects a flow of people based on the image. (Step S13) The detection unit 130 detects the position of the door 400 based on the position where the flow of people occurs. (Step S14) The detection unit 130 detects the range where people appear as the width of the door 400 based on the image.

[0038] (Step S15) The detection unit 130 detects the lower edge portion of the door 400 based on one of the plurality of images constituting the image and the position of the door 400. (Step S16) The detection unit 130 detects the stride of a person using the image. (Step S17) The specifying unit 140 specifies a door closing determination region based on the width of the door 400 and the stride with reference to the lower edge portion of the door 400.

[0039] Next, the processing after the door closing determination region is specified will be described. FIG. 9 is a flowchart showing an example of the door closing determination process. (Step S21) The acquisition unit 120 acquires an image from the camera 200. (Step S22) The determination unit 150 sets the door closing determination region in the image. (Step S23) The determination unit 150 determines whether a person exists in the door closing determination region. If a person exists in the door closing determination region, the process proceeds to step S21. If a person does not exist in the door closing determination region, the process proceeds to step S24.

[0040] (Step S24) The determination unit 150 determines whether or not a predetermined time has elapsed since a person is not present in the door closing determination area. If the predetermined time has elapsed, the process proceeds to step S25. If the predetermined time has not elapsed, the process proceeds to step S21. (Step S25) The determination unit 150 instructs the door drive device 300 to close the door 400. Thereby, the door drive device 300 closes the door 400.

[0041] According to the embodiment, the information processing device 100 automatically identifies the door closing determination area. Therefore, the user does not have to perform the task of identifying the door closing determination area. Thus, the information processing device 100 can reduce the burden on the user.

[0042] In the above description, the case where the door closing determination area is identified using the information processing device 100 that controls the door drive device 300 has been described. The device that identifies the door closing determination area does not have to be a device that controls the door drive device 300. For example, a cloud server may identify the door closing determination area. In this case, the information processing device 100 is the cloud server. Then, after the cloud server identifies the door closing determination area, it transmits the information of the door closing determination area to the device that controls the door drive device 300. Thereby, the device that controls the door drive device 300 can realize the process of FIG. 9.

Description of Reference Numerals

[0043] 10 Image, 11 Train, 12 Person, 13 Visual Impairment Guidance Block, 14 Door Position, 20 Image, 21, 22 Bounding Box, 23 Double Arrow, 30 Image, 31, 32 Persons, 33 Door Position, 34 Line, 35 Lower Edge Portion, 36 Stride, 40 Image, 41 Lower Edge Portion, 42 Width, 43 Stride, 44 Door Closing Judgment Region, 100 Information Processing Apparatus, 101 Processor, 102 Volatile Memory Device, 103 Non-Volatile Memory Device, 104 Communication Interface, 110 Storage Unit, 120 Acquisition Unit, 130 Detection Unit, 140 Identification Unit, 150 Judgment Unit, 200 Camera, 300 Door Driving Device, 400 Door.

Claims

1. An acquisition unit that acquires an image including a person near a door; Based on the image, a flow of people is detected, based on the position where the flow of people occurs, the position of the door is detected, based on the image, the occurrence range of people is detected as the width of the door, and based on one image among a plurality of images constituting the image and the position of the door, the lower side of the door is detected, and using one image among a plurality of images constituting the image, a detection unit that detects the stride of a person; A specifying unit that specifies a door closing determination area, which is an area for determining whether to close the door, based on the width of the door and the stride with reference to the lower side of the door; An information processing apparatus having the above.

2. The door is a door of a train, a platform door of a station, or a door existing in a building, The information processing apparatus according to Claim 1.

3. The detection unit detects a plurality of strides based on one or more images constituting the image, and detects an average value of the plurality of strides or a representative value of the plurality of strides as the stride. The information processing apparatus according to Claim 1 or 2.

4. When specifying the door closing determination area, the specifying unit adds a constant to at least one of the width of the door and the stride to specify the door closing determination area. The information processing apparatus according to Claim 1 or 2.

5. An information processing apparatus acquires an image including a person near a door, based on the image, a flow of people is detected, based on the position where the flow of people occurs, the position of the door is detected, based on the image, the occurrence range of people is detected as the width of the door, and based on one image among a plurality of images constituting the image and the position of the door, the lower side of the door is detected, and using one image among a plurality of images constituting the image, a stride of a person is detected, with reference to the lower side of the door, based on the width of the door and the stride, a door closing determination area, which is an area for determining whether to close the door, is specified, A specifying method.

Images