Data processing apparatus, data processing method, and program

Abstract

It is desired to provide a technique capable of determining whether or not a detection target exists in a detection area with high accuracy.SOLUTION: There is provided a data processing apparatus including a first acquisition unit configured to acquire, from a storage unit, first point cloud data of a detection area in which whether or not a detection target exists is determined, a second acquisition unit configured to acquire second point cloud data of the detection area obtained by a sensor, and a data generation unit configured to generate detection data for determining whether or not the detection target exists in the detection area based on the first point cloud data and the second point cloud data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a data processing device, a data processing method, and a program. [Background technology]

[0002] In recent years, a technology has become known that determines whether a detection target is present within a detection area. An example of the detection target is a person. Another example of the detection area may be an area where railroad tracks are laid near a station platform. Patent Document 1 discloses a technology that determines whether a person has fallen from a station platform by determining whether a person is present within the detection area thus set.

[0003] Here, the determination of whether or not the detection target is present within the detection area is generally made by storing data obtained by the sensor at a point in time in the past as background data, and then calculating the difference between the stored background data and the most recent data obtained by the sensor. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-6775 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in a technology that uses only data acquired by a sensor at a single point in time in the past as background data, changes in the sensor's measurement range between the time of the previous point in time and the acquisition of the latest data are not reflected in the background data, making it impossible to accurately determine whether or not a detection target is present within the detection area.

[0006] Therefore, the present invention has been made in consideration of the above problems, and an object of the present invention is to provide a technology that can determine with high accuracy whether or not a detection target is present within a detection area. [Means for solving the problem]

[0007] In order to solve the above problem, according to one aspect of the present invention, there is provided a data processing device comprising: a first acquisition unit that acquires, from a memory unit, first point cloud data of a detection area in which it is determined whether or not a detection target exists; a second acquisition unit that acquires second point cloud data of the detection area obtained by a sensor; and a data generation unit that generates detection data for determining whether or not the detection target exists in the detection area based on the first point cloud data and the second point cloud data.

[0008] The detection data may include first detection data, the second acquisition unit may acquire the second point cloud data when no moving object is present within a predetermined range, and the data generation unit may generate the first detection data based on the first point cloud data and the second point cloud data.

[0009] The second acquisition unit may acquire current point cloud data of the detection area obtained by the sensor after the second point cloud data is obtained by the sensor, and the data processing device may include a determination unit that performs a determination process to determine whether the detection target exists within the detection area based on the difference between the current point cloud data and the first detection data when a moving object does not exist within the specified range.

[0010] The detection data may include second detection data, the second acquisition unit may acquire the second point cloud data when no moving object is present within a predetermined range, the data processing device may include a third acquisition unit that acquires third point cloud data of the detection area obtained by the sensor when a moving object is present within the predetermined range, and the data generation unit may generate the second detection data based on the first point cloud data, the second point cloud data, and the third point cloud data.

[0011] The second acquisition unit may acquire current point cloud data of the detection area obtained by the sensor after the second point cloud data and the third point cloud data are obtained by the sensor, and the data processing device may include a determination unit that performs a determination process to determine whether the detection target exists within the detection area based on a difference between the current point cloud data and the second detection data when a moving object exists within the specified range.

[0012] The specified range may include a range from the front position of the moving body when the front of the moving body reaches one end of a station platform to the rear end position of the moving body when the rear end of the moving body reaches the other end of the platform.

[0013] The second acquisition unit may acquire current point cloud data of the detection area obtained by the sensor after the second point cloud data is obtained by the sensor, and the data processing device may include a determination unit that performs a determination process to determine whether the detection target exists within the detection area based on a difference between the current point cloud data and the detection data.

[0014] The second acquisition unit may acquire the second point cloud data at each predetermined data generation cycle, and the data generation unit may generate the sensing data at each predetermined data generation cycle.

[0015] The data generation unit may generate the detection data as the current detection data based on the arrival of the current data generation timing and store the current detection data in a memory unit, and the determination unit may perform the determination process based on the difference between the current point cloud data and the current detection data stored in the memory unit until the next data generation timing arrives.

[0016] The data generation unit may store the current detection data in the memory unit by updating the previous detection data stored in the memory unit when the previous data generation timing arrives with the current detection data.

[0017] The detection area may include a time judgment area, the current point cloud data may include current point cloud data of the time judgment area, and the detection data may include past point cloud data of the time judgment area, and the determination unit may identify the difference between the current point cloud data of the time judgment area and the past point cloud data of the time judgment area as an object within the time judgment area, and determine whether the detection target is present within the detection area based on whether the object's stay time within the time judgment area exceeds a threshold.

[0018] The detection area may include a size determination area, the current point cloud data may include current point cloud data of the size determination area, and the detection data may include past point cloud data of the size determination area, and the determination unit may identify a difference between the current point cloud data of the size determination area and the past point cloud data of the size determination area as an object within the size determination area, and determine whether the detection target exists within the detection area based on whether the object exceeds a predetermined size.

[0019] The size determination area may be an area that includes a position higher than the floor surface of the platform of a station where the mobile object stops.

[0020] The detection area may include an area between a station platform and a mobile object stopping at the station.

[0021] In addition, according to another aspect of the present invention to solve the above problem, there is provided a data processing method executed by a computer, including: acquiring, from a storage unit, first point cloud data of a detection area in which it is determined whether or not a detection target exists; acquiring second point cloud data of the detection area obtained by a sensor; and generating detection data for determining whether or not the detection target exists in the detection area based on the first point cloud data and the second point cloud data.

[0022] In addition, according to another aspect of the present invention, in order to solve the above problem, there is provided a program that causes a computer to function as a first acquisition unit that acquires, from a memory unit, first point cloud data of a detection area in which it is determined whether or not a detection target exists, a second acquisition unit that acquires second point cloud data of the detection area obtained by a sensor, and a data generation unit that generates detection data for determining whether or not the detection target exists in the detection area based on the first point cloud data and the second point cloud data. [Effects of the Invention]

[0023] As described above, the present invention provides a technique that can determine with high accuracy whether or not a detection target is present in a detection area. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a block diagram illustrating an example of the configuration of a data processing system according to an embodiment of the present invention. [Figure 2] 1 is a block diagram showing an example of a functional configuration of a data processing device 10 according to an embodiment of the present invention. [Figure 3] FIG. 10 is a diagram showing a situation A1 around a platform H1 of a station at the time when the state of the occupied section changes from an unoccupied state to an occupied state. [Figure 4]FIG. 10 is a diagram showing a situation A4 around a platform H1 of a station at the time when the state of an occupied section C1 changes from an occupied state to an unoccupied state. [Figure 5] FIG. 10 is a diagram for explaining generation of data for detecting a fall when not on the rail. [Figure 6] FIG. 10 is a diagram for explaining generation of data for detecting a fall while on the train. [Figure 7] FIG. 10 is a diagram showing an example of a non-occupied train detection area that is set when the state of an occupied train section C1 is a non-occupied train state. [Figure 8] FIG. 10 is a diagram showing an example of an on-rail detection area that is set when the state of the on-rail section C1 is an on-rail state. [Figure 9] 1 is a flowchart showing an example of processing executed by the data processing system 1 according to the embodiment of the present invention. [Figure 10] 1 is a diagram showing a hardware configuration of an information processing device 900 as an example of a data processing device 10 according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant explanations will be omitted.

[0026] <1. Details of the embodiment> Next, details of the embodiment of the present invention will be described.

[0027] [1-1. Example of data processing system configuration] First, an example of the functional configuration of a data processing system according to an embodiment of the present invention will be described. Fig. 1 is a block diagram showing an example of the configuration of a data processing system according to an embodiment of the present invention. As shown in Fig. 1, the data processing system 1 according to an embodiment of the present invention includes a data processing device 10, a distance measuring device 20, a track signal receiving device 30, and an emergency light device 40. The data processing device 10, the distance measuring device 20, the track signal receiving device 30, and the emergency light device 40 may be connected via a network.

[0028] (Data processing device 10) 2 is a block diagram showing an example of the functional configuration of a data processing device 10 according to an embodiment of the present invention. The data processing device 10 can be realized by a computer. As shown in FIG. 2, the data processing device 10 includes a control unit 110 and a storage unit 190.

[0029] Control unit 110 includes a processor such as a CPU (Central Processing Unit), and its functions can be realized by the CPU expanding a program stored in a non-volatile storage device into RAM (Random Access Memory) and executing it. In this case, a computer-readable recording medium on which the program is recorded can also be provided. Alternatively, control unit 110 can be configured with dedicated hardware or a combination of multiple pieces of hardware.

[0030] The control unit 110 includes an on-rail / off-rail determination unit 120, a distance measurement data acquisition unit 130, a base background data acquisition unit 140, a data generation unit 150, a fall detection unit 160, and a notification control unit 170. The distance measurement data acquisition unit 130 includes an on-rail distance measurement data acquisition unit 131 and an on-rail distance measurement data acquisition unit 132. The data generation unit 150 includes an on-rail fall detection data generation unit 151 and an on-rail fall detection data generation unit 152. The fall detection unit 160 includes an on-rail fall detection unit 161 and an on-rail fall detection unit 162. Details of these components included in the control unit 110 will be described later.

[0031] The storage unit 190 is a storage device capable of storing programs and data for operating the control unit 110. The storage unit 190 can also temporarily store various data required in the course of operation of the control unit 110. For example, the storage device may be a non-volatile storage device.

[0032] (Distance measuring device 20) The distance measuring device 20 is a distance measuring sensor that obtains distance measurement data within a measurement range. The distance measuring device 20 then outputs the distance measurement data to the data processing device 10. More specifically, the distance measuring device 20 obtains the three-dimensional coordinates of an object within the measurement range (the distance from the distance measuring device 20 to the object for each two-dimensional coordinate) as distance measurement data. The distance measurement data includes the distance from the distance measuring device 20 to the object for each two-dimensional coordinate as a set of points (hereinafter also referred to as a "point cloud"). Therefore, the distance measurement data may correspond to point cloud data. An object existing in real space can be detected three-dimensionally from the point cloud.

[0033] For example, the distance measuring device 20 may be a LiDAR (Laser Imaging Detection and Ranging) that emits laser light, detects reflected light of the laser light, and measures distance based on information about the reflected light. LiDAR is also referred to as a laser sensor, and repeatedly scans a laser light two-dimensionally from the start point to the end point to continuously obtain scanning results (hereinafter also referred to as "frames").

[0034] However, various sensors other than LiDAR may be used as the distance measuring device 20. For example, a millimeter wave radar that uses millimeter waves instead of laser light may be used as the distance measuring device 20. Alternatively, a sonar that uses sound waves instead of laser light may be used as the distance measuring device 20.

[0035] The ranging device 20 is installed at a position where it can obtain ranging data of the detection area for determining whether or not a detection target exists. In the embodiment of the present invention, it is mainly assumed that the detection target is a person, the moving object is a train, and the detection area is set to include at least the area between a station platform and the moving object stopped at the station. It is also mainly assumed that it is determined whether or not a detection target exists in the detection area based on the ranging data of the detection area obtained by the ranging device 20.

[0036] This allows a person falling from the station platform to be detected, and measures can be taken to prevent an accident in which a person comes into contact with a train. However, the location where the distance measuring device 20 is installed may be changed depending on the location of the detection area, etc. Examples of locations where the distance measuring device 20 may be installed will be described in detail later.

[0037] A person is an example of a detection target. Therefore, objects other than a person can also be detection targets. For example, animals or inanimate objects other than a person (e.g., robots) can also be detection targets. Furthermore, a train is an example of a moving object. Therefore, objects other than trains can also be moving objects, and various objects that may come into contact with people can be moving objects. For example, vehicles other than trains (e.g., buses, automobiles, etc.) or ships can be moving objects. Note that a train may be made up of multiple cars coupled together, or it may be a single car.

[0038] (Train signal receiving device 30) When a train is present within a predetermined range, the train presence signal receiving device 30 receives a signal indicating that a train is present within the predetermined range.

[0039] In the following description, the specified range will also be referred to as the "train occupied section." The state in which a train is present in a train occupied section will also be referred to as the "train occupied state," and the state in which a train is not present in a train occupied section will also be referred to as the "train not occupied state." Furthermore, a signal indicating that a train is present in a specified range will also be referred to as the "train occupied signal." Information corresponding to whether a train occupied section is in the train occupied state or the train not occupied state will also be simply referred to as the "state of the train occupied section."

[0040] Figure 3 is a diagram showing the situation A1 around platform H1 of the station at the time when the state of the occupied section changes from an unoccupied state to an occupied state. As shown in Figure 3, the occupied section C1 may include the range from the front position of train T1 when the front of train T1 arrives at one end E1 (Figure 3) of platform H1 of the station to the rear end position of train T1 when the rear of train T1 arrives at the other end E2 (Figure 3) of platform H1 of the station. At this time, the track occupation signal receiving device 30 can receive the signal when train T1 is present on the tracks of the station.

[0041] The track presence signal receiving device 30 determines whether or not it is receiving a track presence signal at a predetermined reception determination period. If it determines that it is receiving a track presence signal, it outputs the track presence signal to the data processing device 10. When the track presence signal is input to the data processing device 10, the data processing device 10 determines that the state of the track presence section C1 is a track presence state. An example of a method by which the track presence detection device N1 transmits a track presence signal will be described in detail later.

[0042] On the other hand, when the track presence signal receiving device 30 determines that it has not received a track presence signal, it outputs a non-track presence signal to the data processing device 10. When the non-track presence signal is input to the data processing device 10, the data processing device 10 determines that the state of the track presence section C1 is a non-track presence state.

[0043] Alternatively, it may be assumed that the track presence detection device N1 is capable of transmitting a non-track presence signal. In such a case, the track presence signal receiving device 30 may determine whether or not it is receiving a non-track presence signal at a predetermined reception determination period. Then, when it determines that it is receiving a non-track presence signal transmitted from the track presence detection device N1, the track presence signal receiving device 30 may output the non-track presence signal to the data processing device 10.

[0044] (Emergency light device 40) The emergency light device 40 notifies the user that a person is present in the detection area based on the data processing device 10 determining that a person is present in the detection area. As an example, the presence of a person in the detection area may mean that the person has fallen from the station platform H1. In the following description, the determination process performed by the data processing device 10 to determine whether a person is present in the detection area is also referred to as a "fall detection process."

[0045] For example, the emergency light device 40 may be configured to include a lamp and may notify that it has been determined that a person is present in the detection area by turning on the lamp. Alternatively, the emergency light device 40 may be configured to include a monitor and may notify that it has been determined that a person is present in the detection area by displaying a predetermined color (e.g., red) on the monitor. Alternatively, the emergency light device 40 may notify that a person is present in the detection area by outputting a predetermined sound (warning sound) from a speaker instead of the emergency light device 40.

[0046] The location where the emergency lighting device 40 is installed is not particularly limited. For example, the emergency lighting device 40 may be installed on the station platform H1, or in a station office where station staff are present. Alternatively, the emergency lighting device 40 may be installed in a monitoring center where staff are present to monitor the operation status of the train T1.

[0047] The above describes an example of the configuration of the data processing system 1 according to the embodiment of the present invention.

[0048] [1-2.Details of the data processing system functions] Next, detailed functions of the data processing system 1 according to the embodiment of the present invention will be described. In the data processing system 1 according to the embodiment of the present invention, base background data of a detection area is stored in the storage unit 190. The base background data is distance measurement data of the detection area obtained by the distance measuring device 20 when no person was present in the detection area at a point in the past. The base background data may correspond to first point cloud data.

[0049] The base background data acquisition unit 140 acquires base background data of the detection area from the storage unit 190. Then, the ranging data acquisition unit 130 acquires ranging data of the detection area obtained by the ranging device 20 from the ranging device 20. The ranging data of the detection area obtained by the ranging device 20 may correspond to the second point cloud data. For example, the first point cloud data is the base background data, whereas the second point cloud data is background data when a train is in operation and is background data obtained by the ranging device 20 when the train is not on the line.

[0050] The data generation unit 150 generates fall detection data for determining whether or not a person is present in the detection area, based on the base background data acquired by the base background data acquisition unit 140 and the distance measurement data acquired by the distance measurement data acquisition unit 130. The fall detection data may correspond to detection data.

[0051] With this configuration, not only the base background data obtained by the distance measuring device 20 at a certain point in the past, but also any changes that have occurred in the measurement range of the distance measuring device 20 from that certain point in the past until the latest distance measurement data is obtained are reflected in the fall detection data. Therefore, with this configuration, it becomes possible to determine with high accuracy whether or not a person is present in the detection area.

[0052] The distance measurement data acquisition unit 130 acquires distance measurement data of the detection area obtained by the distance measurement device 20 from the distance measurement device 20 at a predetermined data generation cycle, and the data generation unit 150 generates fall detection data at a predetermined data generation cycle. Meanwhile, the fall detection unit 160 performs fall detection processing at a predetermined fall detection processing cycle. The data generation cycle and the fall detection processing cycle are assumed to be different. For example, the data generation cycle may be longer than the fall detection processing cycle.

[0053] Therefore, the data generating unit 150 may generate the current fall detection data based on the arrival of the current data generation timing, and store the current fall detection data in the storage unit 190. Then, the fall detection unit 160 may perform the fall detection process based on the difference between the current distance measurement data of the detection area obtained by the distance measuring device 20 and the current fall detection data stored in the storage unit 190, until the next data generation timing arrives.

[0054] More specifically, when the timing for generating the previous data arrives, the data generating unit 150 stores the previous data for fall detection in the storage unit 190. Therefore, based on the generation of the current data for fall detection, the data generating unit 150 may update the previous data for fall detection stored in the storage unit 190 with the current data for fall detection. In this way, the data generating unit 150 may store the current data for fall detection in the storage unit 190.

[0055] FIG. 4 is a diagram showing a situation A4 around a platform H1 of a station at the time when the state of an occupied section C1 changes from an occupied state to an unoccupied state.

[0056] First, as train T1 approaches the station, the state of occupied section C1 changes from an unoccupied state to an occupied state (Figure 3). Next, train T1 stops at the station to allow passengers to board and disembark between train T1 and station platform H1. Next, when it is time for train T1 to depart the station, train T1 begins to move. Then, the state of occupied section C1 changes from an occupied state to an unoccupied state (Figure 4).

[0057] The ranging data acquisition unit 130 acquires ranging data from the ranging device 20 at predetermined time intervals. The ranging data is acquired in units of frames. That is, the predetermined time interval corresponds to the interval between frames acquired by the ranging data acquisition unit 130. The ranging data acquisition unit 130 stores the acquired ranging data in the memory unit 190. The memory unit 190 stores a predetermined number of the most recent frames. Situation A1 and situation A4 will be described in detail below.

[0058] (Transition from non-railway state to railway state) Situation A1 is shown in Figure 3. As described above, situation A1 is the situation around platform H1 of the station at the time when the state of occupied section C1 changes from an unoccupied state to an occupied state.

[0059] The track presence / non-presence determining unit 120 determines whether the state of the track occupied section C1 is the track presence state at every predetermined reception determination period, depending on whether the track presence signal is received by the track presence signal receiving device 30. After the state of the track occupied section C1 changes from the non-track presence state to the track presence state, the track presence signal is transmitted by the track presence detection device N1 and received by the track presence signal receiving device 30 until the state of the track occupied section C1 changes from the track presence state to the non-track presence state. Therefore, the track presence / non-presence determining unit 120 determines that the state of the track occupied section C1 is the track presence state.

[0060] The method by which the track presence detection device N1 transmits the track presence signal is not limited. As an example, the track presence state may be detected by a track circuit and the track presence signal may be transmitted. The track circuit is configured to include the track presence detection device N1, a power supply device N2, a left rail L1, and a right rail L2. The track presence detection device N1 is electrically connected to portions of the left rail L1 and the right rail L2 that are located at one end of the track presence section C1. Furthermore, the power supply device N2 is electrically connected to portions of the left rail L1 and the right rail L2 that are located at the other end of the track presence section C1.

[0061] When train T1 is not present in the track section C1, the current supplied from power supply N2 flows mainly through the right rail L2, track presence detection device N1, and left rail L1, resulting in a large current detected by track presence detection device N1. On the other hand, when train T1 is present in the track section C1, the current supplied from power supply N2 flows mainly through the right rail L2, the wheelset of train T1, and the left rail L1, resulting in a small current detected by track presence detection device N1.

[0062] Therefore, when the track presence detection device N1 detects a current smaller than a predetermined current or when it does not detect any current, it determines that the train T1 is present in the track presence section C1 and transmits a track presence signal. In this way, a track circuit can be configured.

[0063] (Transition from on-rail state to off-rail state) Situation A4 is shown in Figure 4. As described above, situation A4 is the situation around platform H1 of the station at the time when the state of occupied section C1 changes from an occupied state to an unoccupied state.

[0064] The track presence / non-presence determining unit 120 determines whether the state of the track occupied section C1 is the track presence state at every predetermined reception determination period, depending on whether the track presence signal is received by the track presence signal receiving device 30. After the state of the track occupied section C1 changes from the track presence state to the non-presence state, no track presence signal is transmitted by the track presence detection device N1 and received by the track presence signal receiving device 30 until the state of the track occupied section C1 changes from the non-presence state to the track presence state. Therefore, the track presence / non-presence determining unit 120 determines that the state of the track occupied section C1 is the non-presence state.

[0065] (Data generation when not on the line) The fall detection data generated by the data generating unit 150 may include data for detecting a fall when not present on the rail. The data for detecting a fall when not present on the rail may correspond to the first detection data.

[0066] When the occupied / non-occupied track determination unit 120 determines that the state of the occupied track section C1 is a non-occupied track state, the non-occupied track distance measurement data acquisition unit 131 acquires the distance measurement data obtained by the distance measurement device 20 as non-occupied track distance measurement data. The non-occupied track fall detection data generation unit 151 generates non-occupied track fall detection data based on the base background data and the non-occupied track distance measurement data. The generation of non-occupied track fall detection data will be described with reference to Figure 5.

[0067] FIG. 5 is a diagram for explaining the generation of data for detecting falls while not on the rail. Referring to FIG. 5, a base image 51A is shown. In the base image 51A, the gray areas represent objects within the measurement range of the distance measuring device 20, and the objects within the measurement range mainly include station platforms and road surfaces on which tracks are laid. In addition, in the base image 51A, the thick line areas represent base background data. The base background data is distance measurement data from the distance measuring device 20 to the object surfaces within the measurement range (mainly the platform surface D1 and the road surface D2 without snow).

[0068] 5, in order to clearly represent the position of the object surface at which the distance measurement data from the distance measuring device 20 is measured, the distance measurement data in the base image 51A is represented from a viewpoint not at the position of the distance measuring device 20 but at a position outside the distance measuring device 20. The base background data 52A is data obtained by excluding the gray parts (objects within the measurement range) from the base image 51A and leaving only the base background data portion, and is acquired from the memory unit 190 by the base background data acquisition unit 140.

[0069] Furthermore, referring to Figure 5, a non-occupancy image 51B is shown. In the non-occupancy image 51B, the gray areas are also objects within the measurement range of the distance measuring device 20, and the objects within the measurement range include station platforms as well as changes that have occurred in the measurement range of the sensor, such as snow accumulated on the road surface. In the non-occupancy image 51B, the bold lines represent distance measurement data when a vehicle is not present on the line. The distance measurement data when a vehicle is not present on the line is distance measurement data from the distance measuring device 20 to the surface of objects within the measurement range (mainly the platform surface D1 and the surface D3 of snow accumulated on the road surface).

[0070] As with the base image 51A, the distance measurement data in the non-on-rail image 51B is expressed not with the position of the distance measuring device 20 as the viewpoint, but with a position outside the distance measuring device 20 as the viewpoint.

[0071] The data generation unit 151 for detecting falls when a train is not present on the railway line acquires the base background data 52A stored in the memory unit 190, and can generate data for detecting falls when a train is not present on the railway line 53P by adding the acquired base background data 52A and the distance measurement data for when a train is not present on the railway line 52B newly obtained by the distance measurement device 20. The data generation unit 151 for detecting falls when a train is not present on the railway line 53P and the distance measurement data for when a train is not present on the railway line 52B are stored in the memory unit 190. The data for detecting falls when a train is not present on the railway line 53P generated in this manner includes distance measurement data from the distance measurement device 20 to the surfaces of objects within the measurement range (mainly the platform surface D1, the road surface D2 without snow, and the surface D3 of snow accumulated on the road surface).

[0072] As a result, any changes (environmental changes such as snowfall) that occur within the measurement range after the base background data 52A is obtained are reflected in the data for detecting falls while not present on the rails 53P. Therefore, the changes will no longer be erroneously detected as the difference between the latest distance measurement data obtained by the distance measuring device 20 and the data for detecting falls while not present on the rails 53P, and it is expected that the detection accuracy of the detection target will improve.

[0073] (Data generation while on the line) The fall detection data generated by the data generating unit 150 may include data for detecting a fall while the train is on the rail. The data for detecting a fall while the train is on the rail may correspond to the second detection data.

[0074] When the on-train / off-train determination unit 120 determines that the state of the on-train section C1 is an on-train state, the on-train distance measurement data acquisition unit 132 acquires the distance measurement data obtained by the distance measurement device 20 as on-train distance measurement data. The on-train distance measurement data may correspond to third point cloud data. The on-train fall detection data generation unit 152 generates on-train fall detection data based on the base background data, the off-train distance measurement data, and the on-train distance measurement data. The generation of on-train fall detection data will be described with reference to FIG. 6.

[0075] FIG. 6 is a diagram for explaining the generation of data for detecting falls while on the track. Referring to FIG. 6, in addition to base image 51A, base background data 52A, non-on-track image 51B, and non-on-track distance measurement data 52B, on-track image 51C is also shown. In on-track image 51C, the gray areas represent objects within the measurement range of distance measurement device 20, which mainly includes station platforms and trains. In on-track image 51C, the thick lines represent on-track distance measurement data. On-track distance measurement data is distance measurement data from distance measurement device 20 to the object surfaces within the measurement range (mainly platform surface D1 and train body surface D4).

[0076] As with the base image 51A and the non-on-rail image 51B, the distance measurement data in the on-rail image 51C is expressed from the viewpoint of a position outside the distance measuring device 20, rather than from the viewpoint of the position of the distance measuring device 20.

[0077] The data generation unit 152 for detecting falls while on a train acquires the base background data 52A and the distance measurement data when not on a train 52B stored in the memory unit 190, and can generate the data for detecting falls while on a train 53Q by adding together the acquired base background data 52A and the distance measurement data when not on a train 52B and the distance measurement data when not on a train 52C newly acquired from the distance measurement device 20. Note that the data generation unit 152 for detecting falls while on a train may also generate the data for detecting falls while on a train 53Q by adding together the data for detecting falls while not on a train 53P stored in the memory unit 190 and the distance measurement data when on a train 52C newly acquired from the distance measurement device 20.

[0078] The data 53Q for detecting falls while on the train generated in this manner will include distance measurement data from the distance measuring device 20 to the object surfaces within the measurement range (mainly the platform surface D1, the road surface without snow D2, the surface of snow accumulated on the road D3, and the train body surface D4).

[0079] As a result, changes that have occurred within the measurement range since the base background data 52A was obtained (environmental changes such as snowfall and changes from a non-occupied state to an on-rail state, etc.) are reflected in the on-rail fall detection data 53Q. Therefore, the changes will not be erroneously detected as the difference between the latest distance measurement data obtained by the distance measuring device 20 and the on-rail fall detection data 53Q, and it is expected that the detection accuracy of the detection target will be improved. Note that the environmental change does not have to be limited to snowfall. For example, the environmental change may also include the growth of plants on the road surface.

[0080] In particular, when the on-rail distance measurement data 52C is acquired, an obstructed area (such as the road surface D2 without snow and the surface D3 of snow on the road surface) that was obstructed by the train and not included in the measurement range may be included in the measurement range when the fall detection process is executed due to the swaying of the train, etc. Even in such a case, if the obstructed area is reflected in the on-rail fall detection data 53Q, the obstructed area will not be erroneously detected as the difference between the latest distance measurement data obtained by the distance measurement device 20 and the on-rail fall detection data 53Q, and it is expected that the detection accuracy of the detection target will be improved.

[0081] (Fall detection process details) As described above, the data generation unit 150 generates fall detection data. The fall detection data is also referred to as determination process data or fall detection point cloud data, and includes non-rail fall detection data 53P and rail fall detection data 53Q. The fall detection unit 160 functions as a determination unit that performs fall detection processing based on the current ranging data (current point cloud data) of the detection area obtained by the ranging device 20 and acquired from the ranging data acquisition unit 130. The fall detection processing is a determination process that determines whether or not a person is present in the detection area.

[0082] The current distance measurement data is distance measurement data obtained by the distance measurement device 20 after the distance measurement data used to generate the fall detection data was obtained by the distance measurement device 20.

[0083] More specifically, fall detection unit 160 acquires the fall detection data generated by data generation unit 150, and performs fall detection processing based on the difference between the fall detection data and the current distance measurement data for the detection area acquired by distance measurement data acquisition unit 130. When fall detection unit 160 determines that a person is present in the detection area, notification control unit 170 controls emergency light device 40 so that emergency light device 40 notifies that a person is present in the detection area.

[0084] The following explanation mainly focuses on an example in which the fall detection unit 160 sets different detection areas when the state of the occupied section C1 is an unoccupied train state and when the state of the occupied section C1 is an occupied train state. However, the fall detection unit 160 may set the same detection area when the state of the occupied section C1 is an unoccupied train state and when the state of the occupied section C1 is an occupied train state.

[0085] Fig. 7 is a diagram showing an example of a non-occupancy detection area that is set when the state of the occupied section C1 is a non-occupancy state. Referring to Fig. 7, a non-occupancy detection area R1 is shown. In a non-occupancy state, no train is stopped at the station, and the area in which a person who has fallen from the floor F1 of platform H1 may be present is wide horizontally. For this reason, the non-occupancy detection area R1 is set to be wide horizontally.

[0086] Fig. 8 is a diagram showing an example of an on-train detection area that is set when the state of the on-train section C1 is an on-train state. Referring to Fig. 8, an on-train detection area R2 is shown. In the on-train state, train T1 is stopped at the station, and the area in which a person who has fallen from the floor F1 of platform H1 may be present is narrow horizontally. Therefore, on-train detection area R2 is set to be narrow horizontally.

[0087] However, both the non-occupancy detection area R1 and the on-track detection area R2 are set to include the area between the station platform H1 and the train T1 stopping at the station. The measurement range θ of the distance measuring device 20 is indicated by the angle of the measurement range based on the position of the distance measuring device 20. The distance measuring device 20 is installed on the station platform H1 or in the vicinity of the station platform H1 so that both the non-occupancy detection area R1 and the on-track detection area R2 are included in the measurement range θ of the distance measuring device 20.

[0088] (Detection area R1 when not on the track) When the occupied / non-occupied train section C1 is determined to be in a non-occupied train state by the occupied / non-occupied train determination unit 120, the non-occupied train fall detection unit 161 calculates the difference between the current distance measurement data and the non-occupied train fall detection data 53P generated by the non-occupied train fall detection data generation unit 151. Then, the non-occupied train fall detection unit 161 determines whether or not a person is present in the detection area R1 based on the calculated difference.

[0089] The detection area R1 when not present on the rail may include a size determination area R11 for determining the size of an object. The data 53P for detecting a fall when not present on the rail includes distance measurement data for the size determination area R11 when not present on the rail, and the current distance measurement data acquired by the distance measurement data acquisition unit 130 includes the current distance measurement data for the size determination area R11.

[0090] The fall detection unit 160 identifies the difference between the current distance measurement data of the size determination area R11 and the data for fall detection when not on rail 53P as an object within the size determination area R11, and measures the size of the identified object. Note that the size of the object may be the number of difference points, the vertical length or horizontal length of a rectangle surrounding the difference (for example, a rectangle circumscribing the difference), or the length of the longer side of the vertical or horizontal length.

[0091] The fall detection unit 160 may determine whether a person is present in the non-rail detection area R1 based on whether the size of an object exceeds a predetermined size. Note that a person present at a position higher than the floor F1 is expected to have a relatively straight body. Therefore, as shown in FIG. 7, if the size determination area R11 includes an area that is higher than the floor F1 of the station platform H1, it is possible to determine with high accuracy whether a person is present in the non-rail detection area R1.

[0092] The non-occupancy detection area R1 may also include a time determination area R12 for determining the duration of an object's presence. The non-occupancy fall detection data 53P includes base background data and distance measurement data for the time determination area R12 when the object is not present on the rail, and the distance measurement data acquired by the distance measurement data acquisition unit 130 includes the current distance measurement data for the time determination area R12.

[0093] The fall detection unit 160 identifies an object within the time determination area R12 as the difference between the current distance measurement data for the time determination area R12 and the data 53P for fall detection during non-existence of a train within the time determination area R12, and counts the time the object stays within the time determination area R12. Note that the stay time may be the time the object stays continuously within the time determination area R12.

[0094] The fall detection unit 160 may determine whether a person is present in the non-rail detection area R1 based on whether the amount of time an object stays in the time determination area R12 exceeds a threshold. In the example shown in Fig. 7, the entire time determination area R12 is included in the size determination area R11. However, only a portion of the time determination area R12 may be included in the size determination area R11, or there may be no overlapping area between the time determination area R12 and the size determination area R11.

[0095] Either the determination of size in the size determination area R11 or the counting of stay time in the time determination area R12 may be performed alone, or both may be performed in combination. When the determination of size in the size determination area R11 and the counting of stay time in the time determination area R12 are performed in combination, the determination of size in the size determination area R11 and the counting of stay time in the time determination area R12 may be performed in parallel, or one may be performed after the other is finished.

[0096] For example, after the size is determined in the size determination area R11, the staying time in the time determination area R12 may be counted. More specifically, the fall detection unit 160 may track an object when it is determined that the size of the object in the size determination area R11 exceeds a predetermined size. Then, the fall detection unit 160 may determine whether a person is present in the non-occupancy detection area R1 based on whether the staying time of the object in the time determination area R12 exceeds a threshold.

[0097] (Detection area R2 when on track) When the on-train / off-train determination unit 120 determines that the state of the on-train section C1 is the on-train state, the on-train fall detection unit 162 calculates the difference between the current distance measurement data and the on-train fall detection data 53Q generated by the on-train fall detection data generation unit 152. Then, the off-train fall detection unit 161 determines whether or not a person is present in the detection area R2 based on the calculated difference.

[0098] The on-rail detection area R2 may include a size determination area R21 for determining the size of an object, similar to the off-rail detection area R1. The fall detection unit 160 may measure the size of an object within the size determination area R21, and determine whether or not a person is present within the on-rail detection area R2 based on the size of the object, similar to when the person is not present on the rail.

[0099] Similarly to the non-occupancy detection area R1, the on-rail detection area R2 may include a time determination area R22 for determining the dwell time of an object. The fall detection unit 160 may count the dwell time of an object in the time determination area R22, and determine whether or not a person is present in the on-rail detection area R2 based on the dwell time of the object, similar to the non-occupancy detection area R1.

[0100] When a train is present on the line, as when it is not present on the line, either the size determination in the size determination area R21 or the counting of the stay time in the time determination area R22 may be performed alone, or both may be performed in combination.

[0101] The above has described the details of the functions of the data processing system 1 according to the embodiment of the present invention.

[0102] [1-3. Processing example of data processing system 1] 9 is a flowchart showing an example of processing executed by the data processing system 1 according to an embodiment of the present invention. With reference to FIG. 9 (and also with reference to FIGS. 1 to 8 as appropriate), an example of processing executed by the data processing system 1 according to an embodiment of the present invention will be described.

[0103] 9, the base background data acquisition unit 140 acquires the base background data 52A stored in the storage unit 190 from the storage unit 190 (S11). The occupied / non-occupied train determining unit 120 determines the state of the occupied train section C1 (S12).

[0104] When the on-track / off-track determination unit 120 determines that the state of the on-track section C1 is an off-track state ("off-track state" in S13), the off-track distance measurement data acquisition unit 131 acquires the current distance measurement data obtained by the distance measurement device 20 as off-track distance measurement data 52B (S14).

[0105] The data generation unit 151 for detecting falls when the train is not present on the railway generates data 53P for detecting falls when the train is not present on the railway based on the base background data 52A acquired by the base background data acquisition unit 140 and the distance measurement data 52B acquired by the distance measurement data acquisition unit 131 when the train is not present on the railway (S15).

[0106] More specifically, the data generation unit 151 for detecting falls when a train is not present on the rail generates data for detecting falls when a train is not present on the rail 53P by adding together the point cloud included in the base background data 52A and the point cloud included in the distance measurement data when a train is not present on the rail 52B. The data generation unit 151 for detecting falls when a train is not present on the rail 53P and the distance measurement data when a train is not present on the rail 52B are stored in the memory unit 190.

[0107] On the other hand, when the on-line / off-line determination unit 120 determines that the state of the on-line section C1 is on the line ("on-line state" in S13), the on-line distance measurement data acquisition unit 132 acquires the latest off-line distance measurement data 52B stored in the memory unit 190 from the memory unit 190, and acquires the current distance measurement data obtained by the distance measurement device 20 as on-line distance measurement data 52C (S16).

[0108] The data generation unit 151 for detecting falls while the train is not present on the line generates data 53Q for detecting falls while the train is present on the line based on the base background data 52A acquired by the base background data acquisition unit 140, the non-presence distance measurement data 52B acquired by the non-presence distance measurement data acquisition unit 131, and the presence distance measurement data 52C acquired by the presence distance measurement data acquisition unit 132 (S17).

[0109] More specifically, the data generation unit 151 for detecting falls while the vehicle is not present on the rail generates the data for detecting falls while the vehicle is present on the rail 53Q by adding together the point cloud included in the base background data 52A, the point cloud included in the distance measurement data while the vehicle is not present on the rail 52B, and the point cloud included in the distance measurement data while the vehicle is present on the rail 52C. The data generation unit 151 for detecting falls while the vehicle is not present on the rail 53Q may also generate the data for detecting falls while the vehicle is not present on the rail 53Q by adding together the data for detecting falls while the vehicle is not present on the rail 53P and the distance measurement data while the vehicle is present on the rail 52C. The data generation unit 151 for detecting falls while the vehicle is not present on the rail 53Q stores the data for detecting falls while the vehicle is present on the rail 53Q in the memory unit 190.

[0110] The fall detection unit 160 starts the fall detection process (S18). When the state of the occupied section C1 is the non-occupied state, the fall detection unit 160 determines whether or not a person is present in the detection area R1 based on the difference between the distance measurement data of the detection area R1 obtained by the distance measurement device 20 and the non-occupied fall detection data 53P for the detection area R1. On the other hand, when the state of the occupied section C1 is the occupied state, the fall detection unit 160 determines whether or not a person is present in the detection area R2 based on the difference between the distance measurement data of the detection area R2 obtained by the distance measurement device 20 and the on-track fall detection data 53Q for the detection area R2.

[0111] The steps from acquiring base background data (S11) to generating fall detection data (S15 or S17) are executed at a predetermined data generation cycle. The fall detection process is executed at a predetermined fall detection processing cycle. However, it is assumed that the data generation cycle is longer than the fall detection processing cycle. In this case, the data generation unit 150 generates fall detection data every time a data generation timing arrives and updates the fall detection data stored in the storage unit 190 with the generated fall detection data, and the fall detection unit 160 executes the fall detection process using the latest fall detection data stored in the storage unit 190 every time a fall detection processing cycle arrives.

[0112] The above describes an example of the processing executed by the data processing system 1 according to the embodiment of the present invention.

[0113] [1-4. Effects of the embodiment] As described above, according to an embodiment of the present invention, a data processing device 10 is provided, which includes a base background data acquisition unit 140 that acquires, from a memory unit 190, base background data of a detection area in which it is determined whether or not a person is present, a ranging data acquisition unit 130 that acquires ranging data of the detection area obtained by the ranging device 20, and a data generation unit 150 that generates fall detection data for determining whether or not a person is present in the detection area based on the base background data and the ranging data.

[0114] With this configuration, not only the base background data obtained by the distance measuring device 20 at a certain point in the past, but also any changes that have occurred in the measurement range of the distance measuring device 20 from that certain point in the past until the latest distance measurement data is obtained are reflected in the fall detection data. Therefore, with this configuration, it becomes possible to determine with high accuracy whether or not a person is present in the detection area.

[0115] The effects of the embodiments of the present invention have been described above.

[0116] <2. Hardware configuration example> Next, an example of the hardware configuration of the data processing device 10 according to the embodiment of the present invention will be described. Below, an example of the hardware configuration of the information processing device 900 will be described as an example of the hardware configuration of the data processing device 10 according to the embodiment of the present invention. Note that the example of the hardware configuration of the information processing device 900 described below is merely one example of the hardware configuration of the data processing device 10. Therefore, the hardware configuration of the data processing device 10 may be such that unnecessary components are deleted from the hardware configuration of the information processing device 900 described below, or new components are added. Note that the hardware of the data processing device 10 can also be realized in a similar manner.

[0117] 10 is a diagram showing the hardware configuration of an information processing device 900 as an example of the data processing device 10 according to an embodiment of the present invention. The information processing device 900 includes a CPU (Central Processing Unit) 901, a ROM (Read Only Memory) 902, a RAM (Random Access Memory) 903, a host bus 904, a bridge 905, an external bus 906, an interface 907, an input device 908, an output device 909, a storage device 910, and a communication device 911.

[0118] The CPU 901 functions as an arithmetic processing unit and control unit, and controls the overall operation of the information processing device 900 in accordance with various programs. The CPU 901 may also be a microprocessor. The ROM 902 stores programs used by the CPU 901, calculation parameters, etc. The RAM 903 temporarily stores programs used in the execution of the CPU 901, parameters that change as appropriate during the execution, etc. These are interconnected by a host bus 904 that is composed of a CPU bus, etc.

[0119] The host bus 904 is connected to an external bus 906, such as a PCI (Peripheral Component Interconnect / Interface) bus, via a bridge 905. It is not necessary to configure the host bus 904, bridge 905, and external bus 906 separately, and these functions may be implemented on a single bus.

[0120] The input device 908 is composed of input means such as a mouse, keyboard, touch panel, buttons, microphone, switches, and levers that allow the user to input information, and an input control circuit that generates an input signal based on the user's input and outputs it to the CPU 901. By operating this input device 908, the user operating the information processing device 900 can input various data to the information processing device 900 and instruct the information processing device 900 to perform processing operations.

[0121] The output device 909 includes, for example, a display device such as a CRT (Cathode Ray Tube) display device, a liquid crystal display (LCD) device, an OLED (Organic Light Emitting Diode) device, or a lamp, and an audio output device such as a speaker.

[0122] The storage device 910 is a device for storing data. The storage device 910 may include a storage medium, a recording device for recording data on the storage medium, a reading device for reading data from the storage medium, and a deletion device for deleting data recorded on the storage medium. The storage device 910 is configured, for example, with an HDD (Hard Disk Drive). This storage device 910 drives a hard disk and stores programs executed by the CPU 901 and various data.

[0123] The communication device 911 is, for example, a communication interface configured with a communication device for connecting to a network, etc. The communication device 911 may be compatible with either wireless communication or wired communication.

[0124] An example of the hardware configuration of the data processing device 10 according to the embodiment of the present invention has been described above.

[0125] <3. Supplementary Information> Although the preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, the present invention is not limited to these examples. It is clear that a person skilled in the art to which the present invention pertains can conceive of various modifications and alterations within the scope of the technical ideas set forth in the claims, and it is understood that these also naturally fall within the technical scope of the present invention.

[0126] The above describes an example in which the data generation unit 151 for detecting falls when a train is not present on the railway generates the data for detecting falls when a train is not present on the railway 53P by adding together the base background data 51A and the distance measurement data for falling when a train is not present on the railway 52B. However, when new distance measurement data for falling when a train is not present on the railway is acquired by the distance measurement data acquisition unit 131 from the distance measuring device 20, the data generation unit 151 for detecting falls when a train is not present on the railway may generate new data for detecting falls when a train is not present on the railway by adding the new distance measurement data for falling when a train is not present on the railway to the data for detecting falls when a train is not present on the railway 53P. [Explanation of symbols]

[0127] 1. Data Processing System 10 Data processing device 20 Ranging device 110 control section 120 Line presence / non-line determination section 130 Distance measurement data acquisition unit 131 Off-line distance measurement data acquisition unit 132 Ranging data acquisition unit while on line 140 Base background data acquisition unit 150 Data Generation Unit 151 Data generation unit for detecting falls when not on the track 152 Data generation unit for detecting falls while on track 160 Fall detection unit 161 Fall detection unit when not on track 162 Fall detection unit while on track 170 Notification control section 190 Storage section

Claims

1. a first acquisition unit that acquires, from a storage unit, first point cloud data of a detection area in which it is determined whether or not a detection target exists; a second acquisition unit that acquires second point cloud data of the detection area obtained by a sensor; a data generation unit that generates detection data for determining whether the detection target exists within the detection area based on the first point cloud data and the second point cloud data; A data processing device comprising:

2. the sensing data includes first sensing data, the second acquisition unit acquires the second point cloud data when no moving object is present within a predetermined range; the data generation unit generates the first detection data based on the first point cloud data and the second point cloud data.

2. The data processing device according to claim 1.

3. the second acquisition unit acquires current point cloud data of the detection area obtained by the sensor after the second point cloud data is obtained by the sensor; the data processing device includes a determination unit that performs a determination process to determine whether the detection target exists within the detection area based on a difference between the current point cloud data and the first detection data when a moving object does not exist within the predetermined range, 3. The data processing device according to claim 2.

4. the sensing data includes second sensing data, the second acquisition unit acquires the second point cloud data when no moving object is present within a predetermined range; the data processing device includes a third acquisition unit that acquires third point cloud data of the detection area obtained by the sensor when a moving object is present within the predetermined range; the data generation unit generates the second detection data based on the first point cloud data, the second point cloud data, and the third point cloud data.

2. The data processing device according to claim 1.

5. the second acquisition unit acquires current point cloud data of the detection area acquired by the sensor after the second point cloud data and the third point cloud data are acquired by the sensor; the data processing device includes a determination unit that performs a determination process to determine whether the detection target exists within the detection area based on a difference between the current point cloud data and the second detection data when a moving object exists within the predetermined range.

5. The data processing device according to claim 4.

6. the predetermined range includes a range from a leading position of the moving body when the leading end of the moving body reaches one end of a station platform to a trailing end position of the moving body when the trailing end of the moving body reaches the other end of the platform, The data processing device according to any one of claims 2 to 5.

7. the second acquisition unit acquires current point cloud data of the detection area obtained by the sensor after the second point cloud data is obtained by the sensor; the data processing device includes a determination unit that performs a determination process to determine whether the detection target exists within the detection area based on a difference between the current point cloud data and the detection data.

2. The data processing device according to claim 1.

8. the second acquisition unit acquires the second point cloud data at a predetermined data generation cycle; the data generation unit generates the detection data at each predetermined data generation cycle.

8. A data processing device according to claim 7.

9. the data generation unit generates the detection data as current detection data based on the arrival of the current data generation timing, and stores the current detection data in a storage unit; the determination unit performs the determination process based on a difference between the current point cloud data and the current detection data stored in the storage unit until the next data generation timing arrives.

9. A data processing device according to claim 8.

10. the data generation unit updates the previous detection data stored in the storage unit when the previous data generation timing arrives with the current detection data, thereby causing the current detection data to be stored in the storage unit.

10. The data processing device according to claim 9.

11. the detection area includes a time determination area, the current point cloud data includes current point cloud data of the time determination area; the detection data includes past point cloud data of the time determination area, the determination unit identifies an object in the time determination area based on a difference between current point cloud data in the time determination area and past point cloud data in the time determination area, and determines whether the detection target exists in the detection area based on whether the stay time of the object in the time determination area exceeds a threshold.

8. A data processing device according to claim 7.

12. the detection area includes a size determination area, the current point cloud data includes current point cloud data of the size determination area; the detection data includes past point cloud data of the size determination area, the determination unit identifies an object within the size determination area based on a difference between current point cloud data of the size determination area and past point cloud data of the size determination area, and determines whether the detection target exists within the detection area based on whether the object exceeds a predetermined size.

8. A data processing device according to claim 7.

13. The size determination area is an area including a position higher than the floor surface of the platform of the station where the moving object stops.

13. A data processing apparatus according to claim 12.

14. The detection area includes an area between a station platform and a moving object stopping at the station.

2. The data processing device according to claim 1.

15. acquiring, from a storage unit, first point cloud data of a detection area in which it is determined whether or not a detection target exists; acquiring second point cloud data of the detection area obtained by a sensor; generating detection data for determining whether the detection target exists within the detection area based on the first point cloud data and the second point cloud data; A computer-implemented data processing method, comprising:

16. Computer, a first acquisition unit that acquires, from a storage unit, first point cloud data of a detection area in which it is determined whether or not a detection target exists; a second acquisition unit that acquires second point cloud data of the detection area obtained by a sensor; a data generation unit that generates detection data for determining whether the detection target exists within the detection area based on the first point cloud data and the second point cloud data; A program that functions as a

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