JP5583511B2 - Passenger entrainment detection system at home - Google Patents

Description

The present invention relates to a passenger entrainment detection system at a home, and in particular, a plurality of MEMS sensors ( area type distance sensors ) functioning as distance sensors for the detection areas set in each are arranged in a row in an upper region along the home edge. The present invention also relates to a passenger entrainment detection system in a home that detects a passenger entrainment accident using the detection operation characteristics of the plurality of MEMS sensors.

As conventional techniques for detecting a fall from a home, there are techniques described in Patent Documents 1 and 2.

The home fall detection device disclosed in Patent Document 1 is arranged at predetermined intervals along the track as shown in FIG. 1, and the track from the home toward the track as shown in FIG. A captured image of each fall detection area is acquired by a plurality of image capturing means provided at an upper position in such an arrangement posture as to look into the track side obliquely. Based on the image data related to the captured image relating to each fall detection area acquired by the plurality of image pickup means, first set the fall detection sample data, and then acquire the current image data, and the current image data and the fall A difference image with the detected sample data is obtained, and the fall of the object is detected based on the number of pixels in the difference area of the difference image. The image imaging means is a so-called imaging camera.

In the home fallen person detection method disclosed in Patent Document 2, a plurality of stereo cameras are arranged at predetermined intervals along the edge of the home, and each detection area of the plurality of stereo cameras is imaged. In the image processing using the image signal obtained in step 1, the falling object from the home is detected, the size of the falling object is obtained, and whether the falling object is a person is determined from the size. .
At this time, in the image processing, the presence of a falling object is determined on the condition that the height is lower than a predetermined height set below the top surface of the home, and the distance from the stereo camera to the falling object is obtained, and the distance is calculated. Based on this, the height of the fallen object is obtained and the number of screens occupied by the fallen object is obtained.

JP 2003-276605 A (FIGS. 1 and 2) JP 2003-246268 A

In the home fall detection device based on the above Patent Documents 1 and 2, an imaging camera (including a stereo camera) is used as detection means to detect an accident such as a fall that occurs on the home based on a captured image. Yes. Since the fall accident is determined based on the number of pixels on the captured image using information related to the image captured by the camera, the person or object that falls on the track is detected, and the fact that it has fallen is detected. However, it was impossible to quickly detect an accident in which a passenger was caught in a train as the train moved.
In addition, image data obtained by camera imaging has a problem that it is not easy to detect a passenger who is affected by ambient light and may be caught.
Automatic detection of passenger entrainment accidents at the time of departure from a train is technically difficult with conventional fall detection devices, and has been performed artificially based exclusively on visual confirmation work.

In view of the above-described problems, an object of the present invention is to detect an accident in which a passenger is involved in a train along a platform edge on a station platform, and can automatically and quickly detect this. The purpose is to provide a passenger entrainment detection system at home.

The passenger entrainment detection system in the home according to the present invention is configured as follows in order to achieve the above object.

The passenger entrainment detection system at the first home (corresponding to claim 1)
Train is Installation along the station home edge of the incoming lines, for each meter measurement time, and at least one area type distance sensor for outputting a detection image of the detection area is set,
And that SL憶means to store data relating to passenger image based on the detection image area type distance sensor outputs,
Processing means for acquiring movement data of the passenger image based on data relating to the passenger image, and determining that a passenger entrainment state has occurred when the movement data indicates movement including rotation in a dangerous area as compared with the determination data ;
It is characterized by having.

In the passenger entrainment detection system at the above-mentioned platform, an area type distance sensor such as a plurality of MEMS sensors each having a detection area set at a predetermined location on the edge of the platform where the train at the station platform enters and stops is arranged. Based on the height information of the detection area obtained by the mold distance sensor, the movement of the passenger in the detection area is detected, and a movement pattern (movement pattern including rotation) peculiar to passenger entrainment is prepared as determination data, It is possible to compare and detect the passenger entrainment state accurately and quickly.

Passenger entrainment detection system in the second home (corresponding to Claim 2), in the above configuration, preferably, the storage means stores data relating to train image based on the detection image output by the area type distance sensor The processing means acquires the positional relationship data between the passenger image and the train image based on the motion data of the passenger image and the data related to the train image, and compares the positional relationship data with the determination data to make the motion data dangerous. It is characterized by determining that a passenger entrainment state has occurred when a movement including rotation in an area is indicated . By detecting based on the positional relationship between the movement of the passenger image and the movement of the train image, it becomes possible to detect a passenger's entrainment state with higher detection accuracy.

A third home Passenger entrainment detection system (corresponding to claim 3), in the above configuration, preferably, the boundary of the detection area of each of the two area type distance sensor adjacent in the plurality of area type distance sensors Are in contact with each other or have an overlapping portion. In setting the detection areas of two adjacent area type distance sensors , the determination algorithm can be easily created by touching or overlapping the boundary portion.

In the fourth home, the passenger entrainment detection system (corresponding to claim 4) is preferably configured as described above, wherein the determination data relates to a passenger motion image in a region related to the dangerous area image in the detection image. It is characterized by being data.

According to the present invention, the area including the platform edge of the station platform is two-dimensionally scanned with a laser beam using area type distance sensors such as a plurality of MEMS sensors, and the height information at the platform edge is obtained. By identifying the passenger image from the change in information and associating it with the movement information of the train, it is possible to automatically and accurately detect the occurrence of the passenger entrainment state from the movement pattern of the passenger image. For this reason, when an accident occurs in which a passenger is involved in a train along a platform edge on a station platform, this can be detected automatically with high accuracy quickly and reliably.

It is a top view which shows the specific arrangement configuration in the home of the sensor system used with the passenger entrainment detection system in the home which concerns on this invention. It is a front view which shows the specific arrangement configuration in the home of a sensor system. It is a figure which shows an example of the detection image obtained by the measurement of one MEMS sensor. It is a block diagram which shows the structure of the detection and determination system of the passenger entrainment detection system in the home which concerns on this invention. It is a figure which shows three examples of the detection image obtained by measurement operation | movement of each MEMS sensor (A) When in a safety area, (B) When in a danger area, (C) The case where it came out to a track area. It is a top view which shows the condition where the passenger has stayed in the vicinity of the said train when the train which entered and stopped at the platform starts again and starts moving. The figure which shows the change state of the detection image of one MEMS sensor (SE1) with progress of measurement time t1, t2, t3 in a time-axis (t) by three change patterns (A), (B), (C). It is. It is a flowchart which shows the flow of the detection operation and determination operation | movement of a passenger entrainment state of the passenger entrainment detection system in a home. It is a figure which shows an example of the preservation | save table at a certain measurement time preserve | saved at a status memory (41). It is a figure which shows an example of the preservation | save table at a certain measurement time preserve | saved at a status memory (42).

  DESCRIPTION OF EMBODIMENTS Preferred embodiments (examples) of the present invention will be described below with reference to the accompanying drawings.

A passenger entrainment detection system at a home according to the present invention is a sensor system for detecting a passenger that may cause a passenger entrainment accident, and whether or not an entrainment state occurs with respect to the detected passenger accurately and quickly. And a determination system having a determination algorithm related to accident detection that is automatically detected.

With reference to FIG. 1 and FIG. 2, the structure regarding the specific arrangement | positioning in the home of a sensor system is demonstrated. FIG. 1 shows a plan view and FIG. 2 shows a front view.

In FIG. 1 and FIG. 2, reference numeral 11 is a station platform viewed from above, and 12 is a train entering the platform 11 in the direction of the arrow as viewed from above. 11A is a home edge formed at a position along the track along which the train 12 enters. The home edge 11A is a boundary between the home 11 and the track. Along the home edge 11A, at a position inside the home edge 11A, a plurality of (for example, n) sensors SE1, SE2,. Is installed on the basis. In FIG. 1 and FIG. 2, seven sensors are shown for convenience. The n sensors SE1, SE2,..., SEn are MEMS sensors. Hereinafter, it is described as “MEMS sensors SE1, SE2,..., SEn”. Here, “MEMS” means the well-known “Micro Electro Mechanical System”. In addition, the “MEMS sensor” includes a configuration of a detection unit that two-dimensionally scans laser light and a configuration of a distance measurement device based on an optical pulse time-of-flight measurement method, and in a specific detection area (two-dimensional region) that is allocated. This is an area sensor that can acquire three-dimensional information ("height information" in this embodiment) based on a distance measuring function. In other words, the “MEMS sensor” is an area type distance sensor. In this embodiment, an example using a MEMS sensor as a typical area type distance sensor will be described. However, the area type distance sensor used in the present invention is not limited to the MEMS sensor, and various types of area type distance sensors are used. be able to.

A plurality of MEMS sensors are mounted on a mounting frame 13 provided upward along the home edge 11A.
Are installed at regular intervals. Each MEMS sensor S installed on the mounting frame 13
E1, SE2,..., SEn are arranged with their detection parts facing downward (or obliquely downward),
A rectangular detection area 14 is set below. The detection area 14 is set at a desired height position from the floor surface of the home 11. Each of the plurality of MEMS sensors SE1, SE2,..., SEn is arranged corresponding to the entrance door 12A of each vehicle of the train 12 which enters the platform 11 and stops. Therefore, the detection area 1 corresponding to each MEMS sensor SE1, SE2,.
4 is normally set so as to include the area of the entrance / exit of each entrance / exit door 12 </ b> A of each vehicle of the train 12. The area such as the entrance / exit of the entrance / exit door 12A is an area where passengers are likely to get involved when the passenger gets in and out. Therefore, the detection area 14 is set so as to include the entrance / exit area. Multiple MEMS sensors SE1, SE2, ..., SEn
Functions as an area sensor for obtaining height information (two-dimensional distance information) of the corresponding detection area 14.

In FIG. 2, two MEMs adjacent to each other by a plurality of MEMS sensors SE1, SE2,.
The relationship between each detection area 14 of S sensor is shown. In this illustrated example, the boundary portion between two adjacent detection areas 14 is set to contact. The boundary part of the detection area 14 can also be set so as to have a common overlapping area. In FIG. 2, 14A shows the scan profile (scan contour) of the outermost laser beam. With respect to the positional relationship between two adjacent detection areas 14, the common area of the boundary portion can be set to almost zero (contact), or the detection areas 14 can be set apart from each other. 2 adjacent
When setting so that the detection areas of two MEMS sensors are separated from each other, a prediction process is required by a determination algorithm based on height information described later.

Each of the n MEMS sensors SE1, SE2,..., SEn installed as described above
At a detection time (measurement time) set at a predetermined time interval, a detection operation related to height information is individually performed as a distance measuring sensor device for the corresponding detection area 14. The height information about the corresponding detection area 14 obtained by the detection operation at each detection time by each MEMS sensor is sent to the processing computer, and based on the height information in the detection area 14 at each detection time by the processing computer. The detected object information for each detection area 14 is acquired. Based on the acquired height information, it is possible to detect the presence of a passenger in the detection area 14, the presence of a passenger car of the train 12, and the like using a threshold value. The detected object information is presence information such as passengers. The acquired height information and detected object information are stored in the memory in a table format as detection information for each of the n MEMS sensors SE1 to SEn for each detection time.

In addition, detection information obtained by each of the n MEMS sensors SE1, SE2,.
According to the detection image), on the processing computer side, regarding the corresponding detection area 14,
Based on the above-mentioned passenger presence information and the pre-position information regarding the installation position, the home edge 1
The position of 1A, the position of the dangerous area near the home edge 11A, and the positions of other safety areas are known.

FIG. 3 shows an example of a detected image obtained by measurement with one MEMS sensor. 21
It is the detection image which showed the corresponding detection area 14 as a two-dimensional rectangular area. This detected image 2
1, 22 is a line image showing the position of the home edge 11 </ b> A, 23 is a danger area image, 24 is a safety area image, and 25 is a passenger image existing in the detection area 14.
In FIG. 3, the upper side of the line image 22 is actually an area on the track track side. In practice, the danger area image 23 is usually an area between the home edge 11A and the approach danger line.

Next, with reference to FIG. 4, the structure of the detection / judgment system of the passenger entrainment detection system in the home which concerns on this invention is demonstrated.

In the passenger entrainment detection system at home, n MEMS sensors SE1, SE2, ...
, SEn time-series image data is acquired from each, and the presence or absence of passengers is automatically detected based on changes in the image contents of these image data as time elapses when the train 12 arrives and departs It has a functional part of a detection / determination system.

The detection / judgment system accurately and promptly determines whether or not a entanglement state has occurred for a passenger based on this memory information, and a memory that extracts and stores passenger and train presence information detected by the sensor system. It is comprised from the arithmetic processing part (CPU) which performs the determination algorithm which concerns on the accident determination automatically detected and determined.

In the configuration shown in FIG. 4, as an example, seven MEMS sensors SE1 to SE7 are shown for convenience. Actually, the output lines of the n MEMS sensors SE1 to SEn are connected to the switch unit 3.
1 is connected. A signal related to the height information of each detection area 14 output from each of the n MEMS sensors SE1 to SEn is input to the switch unit 31 having an input selection type switch function. Output signals of the plurality of MEMS sensors input to the switch unit 31 are selectively captured by the switch unit 31 to the arithmetic processing device 32 side at an appropriate timing. The arithmetic processing unit 32 has three types of memories 41, 42, and 43 and five functional units realized by executing a predetermined program by the CPU, that is, a passenger tracking unit 51, a train home approach detection unit 52, and a train. A stop detection unit 53, a train departure detection unit 54, a passenger entrainment detection start unit 55, and a passenger entrainment determination unit 56 are provided. Further, an output device 61 is provided on the output side of the arithmetic processing device 32.
Is provided.

Each of the memories 41 to 43 is a status memory (state storage memory). The status memory 41 is a memory for storing a state relating to the presence and movement of a passenger (person). The status memory 42 is a memory that stores a state relating to the presence and movement of a train. The status memory 43 stores the combined state of the storage contents of the status memories 41 and 42, and further has a positional relationship between the train and a specific passenger (temporal state change or state transition). Is a memory for storing. The stored data relating to the movement having this relationship is used as basic data for the subsequent determination of passenger entrainment.

  The function contents (processing contents) of the five function units (51 to 56) are as follows.

The passenger tracking unit 51 takes in the detection image 21 output from each of the n MEMS sensors SE1 to SEn at each detection time, and based on the passenger image 25 appearing in the plurality of detection images 21, the passenger image 25 is identical. And tracking the movement of the passenger based on the change in the position of the passenger image 25. Status information relating to the passenger tracked by the passenger tracking unit 51 (specific information of each passenger, position information, position change information, etc.) is stored in the status memory 41.

The train home entry detection unit 52 captures the detection images 21 output from each of the n MEMS sensors SE1 to SEn at each detection time, and a plurality of the detection images 21 located on the train entry side.
2 has a function of detecting the home approach of the train 12 based on the train image appearing on the track side of the line image 22. Normally, the home approach of the train 12 is detected based on the movement of the height information of the front part of the front vehicle of the train 12. The status information related to the train platform approach detected by the train platform entry detector 52 is stored in the status memory 42.

The train stop detection unit 53 captures the detection image 21 output from each of the n MEMS sensors SE1 to SEn at each detection time, and for example, a line image in the plurality of detection images 21 existing at positions corresponding to the head of the train. It has a function of detecting the stop of the train 12 based on the stop operation state of the train image appearing on the 22 track tracks side. Normally, the stop of the train 12 is detected based on the stop operation state based on the height information of the front portion of the front vehicle of the train 12. Train stop detection part 5
The state information relating to the stop of the train detected in 3 is stored in the status memory 42.

The train departure detection unit 54 captures a detection image 21 output from each of the n MEMS sensors SE1 to SEn for each detection time, and for example, a line image 22 in a plurality of the detection images 21 existing at positions corresponding to the head of the train. The train 12 has a function of detecting the departure of the train 12 based on the departure operation state of the train image appearing on the track track side. Usually, the departure of the train 12 is detected based on the operation start state based on the height information of the front portion of the front vehicle of the train 12. Train departure detection unit 54
The status information relating to the departure of the train detected in (1) is stored in the status memory 42.

The passenger entrainment detection start unit 55 includes state information (state data) related to the tracking target passenger stored in the status memory 41, and status information on the home approach, stop, and departure of the train 12 stored in the status memory 42. (State data) is taken in, and processing for associating the state / motion of the passenger on the platform 11 with the state / motion of the train on the track is executed.
The association process by the passenger entrainment detection start unit 55 is executed on condition that the departure signal SI1 related to the departure of the train 12 is input. The process of the association by the passenger entrainment detection start unit 55 is a precondition for starting detection of a state in which the passenger is involved. Passenger and train 1 created by the association process by the passenger entrainment detection start unit 55
The two related state information is stored in the status memory 43.

The passenger entrainment determination unit 56 compares the passenger and train relation state information stored in the status memory 43 with the determination data (danger pattern data) 71, thereby determining the train 1
It is determined whether or not a passenger entrainment state 2 occurs, and when it coincides with the determination data 71, it is detected and determined that a passenger entrainment state has occurred. When the passenger's entrainment state is detected, the passenger entrainment determination unit 56 outputs a detection signal to the output device 61. The output device 61 is, for example, an alarm signal generator or a stop signal generator. The alarm signal generator outputs an alarm signal and generates an alarm sound and an alarm light in a related department or a related peripheral area. The stop signal generator outputs a stop signal such as urgently stopping the train 12 that has started departure at the platform 11 to the train operation control system.

In the description of the present embodiment, the detection and determination of passenger entrainment is structurally as described above.
This is performed by using the passenger and train related state information (information of each position and movement (position change) and relationship) stored in the storage means. On the other hand, in the danger area image 23 of the detected image 21 It can also be configured to detect and determine passenger entrainment using only the information on the position and movement (position change) of the passenger.

Next, the operation of the detection / determination system of the passenger entrainment detection system in the home according to the present invention will be described with reference to FIGS.

As described in FIG. 3, according to the measurement operation of each of the n MEMS sensors SE <b> 1 to SEn, the output information is based on the assumption that the passenger 12 exists in each corresponding detection area 14. Thus, the detection image 21 as shown in FIG. 3 can be obtained. At this time, if a train is present in the detection area 14, a train image is also displayed.

A representative example of a detected image obtained based on the measurement operation of each of the n MEMS sensors SE1 to SEn is shown again in FIG.

The detected image 21 shown in FIG. 5A has exactly the same contents as the detected image described in FIG. In this case, since the passenger image 25 exists in the safety area image 24, it can be determined that the passenger is in a safe state in the positional relationship with the train 12 entering the home 11.

In the detection image 21 shown in FIG. 5B, the passenger image 25 remains in the danger area image 23 and the danger area with respect to the passenger image 25 even though the train 12 starts moving as indicated by the arrow 72. A state of staying and rotating within the image 23 has occurred. Such a detection image 21 means that there is a high possibility that a dangerous state due to the entrainment has actually occurred with respect to the passenger on the home 11. In this case, in particular, when a state in which the passenger rotates (indicating movement relating to the entrainment by the circular arrow 72A indicating the rotation) occurs in the detection image 21, a dangerous state (passenger entrainment state) has occurred. It is judged.

On the other hand, as shown in the detection image 21 in FIG. 5C, when the passenger image 25 existing in the danger area image 23 moves toward the safety area image 24 (the movement is indicated by an arrow 73). Is
For example, it is determined that a passenger getting off from the train 12 has normally moved from a dangerous area near the entrance of the train 12 to the safety area, and is determined to be safe.

The three detection images 21 shown in FIGS. 5A to 5C show detection images obtained by measurement based on a single MEMS sensor. (A) is one detected image at a certain measurement time. (C) makes a determination based on a state (arrow 73) in which the passenger image 25 has moved from the danger area image 23 to the safety area image 24, so that the determination is made by comparing the detected images at at least two measurement times. It is. In the case of (B), it is necessary to detect the rotational motion (arrow 72A) of the passenger image 25, so it is necessary to compare the detected images at at least two measurement times. In the rotation operation (arrow 72A) in the case of (B) and the movement (arrow 73) in the case of (C), it is possible to make a determination based on a detection image obtained by measurement of a related single MEMS sensor.

On the premise of the above measurement operation based on a sensor system composed of n MEMS sensors SE1 to SEn, the operation of the detection / determination system of the passenger entrainment detection system at home according to the present invention is executed.

FIG. 6 shows a situation in which the passenger 74 stays at one place in the vicinity of the train 12 and rotates when the train 12 that enters or stops the platform 11 starts again and starts moving. In FIG. 6, three measurement times (detection times) t1, t2, and t3 are shown as an example regarding the movement of the train 12 and the detection of the movement of the passenger 74. This situation is the result of passenger 7 by train 12
It is assumed that the entrainment state 4 occurs. According to the passenger entrainment detection system at the home according to the present invention, the occurrence state of the passenger entrainment is detected and determined as follows.

In the case of detection / determination of passenger entrainment in the example shown in FIG. 6, in the detection operation of the n MEMS sensors SE1 to SEn at the measurement times t1 to t3, due to the movement of the passenger 74,
In particular, data relating to a detection image obtained by measurement by one MEMS sensor SE2 is used.

FIG. 7 shows the MEMS sensor SE with the passage of measurement times t1, t2, and t3 on the time axis (t).
Three examples (A), (B), and (C) of the change state in the two detected images are shown. Regarding the movement of the passenger 74 in the danger area, (A) shows an example of a change indicating a rapid position movement,
B) shows an example of a change that comes back in contact with the train 12, and (C) shows an example of a change that rotates in contact with the train 12 (involves a passenger). The change pattern of the detected image shown in FIG. 7C is a reference pattern for detecting and determining passenger entrainment.

A variation example (A) in FIG. 7 will be described in detail. At the measurement time t1, the passenger 74 receives the MEMS sensor SE.
The detection image 21 of the MEMS sensor SE2 exists in the vicinity of the home edge 11A of the detection area 14 of No. 2 and becomes an image 81 including the passenger image 25 in the danger area image 23. Since the passenger 74 has moved to the safe area at the measurement time t2, the detection image 21 of the MEMS sensor SE2 becomes an image 82 including the passenger image 25 in the safe area image 24. In the image 82, the passenger image 25 has moved in the direction of the arrow 73. At the measurement time t3, no particular change occurs after the measurement time t2.

As described above, in the detected image 21 obtained by the MEMS sensor SE2 in the lapse of the times t1 and t2, the passenger image 25 moves (changes in position) as indicated by the arrow 73 occurs. In this case, the passenger is involved. It can be determined that the position of the passenger image 25 is abruptly moved in the direction of the safety area, regardless of the occurrence of the state.

A variation example (B) in FIG. 7 will be described in detail. At the measurement time t1, the passenger 74 receives the MEMS sensor SE.
2 present at the boundary between the danger area and the safety area of the detection area 14, and the MEMS sensor SE2
The detected image 21 becomes an image 83 including the passenger image 25 at the boundary of the danger area image 23.
Since the passenger 74 has moved from the danger area to the vicinity of the home edge 11A at the measurement time t2, the detection image 21 of the MEMS sensor SE2 is an image including the passenger image 25 existing on the line image 22 of the danger area image 23. 84. At the measurement time t3, the passenger 74 has moved from the vicinity of the home edge 11A of the dangerous area into the safety area, so the detection image 21 of the MEMS sensor SE2 includes an image 85 including the passenger image 25 existing in the safety area image 24. It becomes.

As described above, the state of movement (position change) of the passenger image 25 as described above occurs in the detection image 21 obtained by the MEMS sensor SE2 in the lapse of time t1, t2, t3. It can be determined that the passenger image 25 returns to the safe area in contact with the train 12 and moves back.

A variation example (C) in FIG. 7 will be described in detail. At the measurement time t1, the passenger 74 receives the MEMS sensor SE.
2 present in the vicinity of the home edge 11A in the danger area of the detection area 14, and the MEMS sensor SE
2 is a passenger image 2 existing on the line image 22 on the track track side of the danger area image 23.
Image 86 including 5. At the measurement time t2, the passenger 74 is brought into contact with the train 12 and the detected image 21 of the MEMS sensor SE2 includes a passenger image 25 having a movement rotating on the line image 22 of the danger area image 23. An image 87 is obtained. Further, at the measurement time t3, the passenger 74 moves while rotating from the vicinity of the home edge 11A of the danger area to the boundary with the safety area, and therefore the detection image 21 of the MEMS sensor SE2 is the danger area image 23 and the safety area image 24. The image 88 includes the passenger image 25 that rotates at the boundary.

As described above, since the change state as described above occurs in the detection image 21 obtained by the MEMS sensor SE2 in the lapse of time t1, t2, t3, the MEMS sensor SE.
By monitoring the change state of the two detection images 21 and detecting the feature change, it is possible to determine whether or not a passenger entrainment state has occurred. That is, in this case, the MEMS sensor SE2
The presence or absence of a passenger entrainment state is determined based on the detected data. More specifically, the characteristic change that there is a passenger image 25 that moves in the vicinity of the danger area image 23 of the detected images (images 86, 87, 88) of the MEMS sensor SE2 at times t1, t2, t3. Based on the state, it is determined that “the occurrence of a passenger entrainment state”.

The content of the characteristic change state shown in FIG. 7C is the determination data 71 described in FIG.
It becomes.

With reference to the flowchart shown in FIG. 8, the flow of the detection operation and determination operation | movement of the passenger entrainment state of the passenger entrainment detection system in a home is demonstrated.

By the steps S11 and S12, n MEMS sensors SE1, SE2,.
The signal detected in is captured. The detection signal is taken in repeatedly at every measurement time set at a predetermined time interval in step S11.

In step S13, a passenger is detected as a detection object based on the height information for each detection area 14 obtained by each of the n MEMS sensors SE1 to SEn, and the state information (specific information of each passenger) related to the passenger is detected. , Position information, position change information, etc.) are stored in the status memory 41 in a table format. FIG. 9 shows an example of a storage table 101 stored in the status memory 41 at a certain measurement time. In this storage table 101, n MEMS sensors SE1 to S1.
For each En, for example, “position”, “detected object”, and “height information” in the detection area 14 are recorded. Here, the “position” is any one of the dangerous area, the safety area, and the outside of the home edge (track track side) in the detection area 14. "Detected object"
Is the presence or absence of a passenger image. “Height information” is height information of the “detected object”, and is used, for example, for determination of an adult or a child. The storage table 101 is created for each measurement time.

Based on the steps S11, S12, S13, the n MEMS sensors SE1, SE
2,..., SEn realizes the above passenger tracking unit 51 for tracking a passenger in a dangerous area or the like set in the vicinity of the home edge 11 </ b> A of the home 11.

Steps S14 to S18 are executed in a parallel processing relationship with respect to step S13. These steps S14 to S18 take in height information for each detection area 14 obtained by each of the n MEMS sensors SE1 to SEn, and n MEMS sensors SE1 to S18.
The train 12 based on the change in the height information with the passage of time in each detection area 14 of En.
Presence / absence (presence) and movement of the train 12 are detected. The detection of the presence of the train 12 is performed by detecting a change in height existing in the region on the track track side outside the line image 22 in the detection image 21 related to the detection area 14 described above. n MEMS sensors SE
The height information for each detection area 14 obtained in each of 1 to SEn is stored in a temporary storage memory for the detected images 21 obtained at a predetermined number of continuous measurement times (step S1).
4) Based on the information related to the height image stored in the storage memory, detection of the home approach operation of the train 12 (step S15), detection of the stop operation (step S16), detection of the departure operation (step S17) Is executed. Information on the detected home approach operation, stop operation, and departure operation relating to the train 12 is stored in a table format in the status memory 42 in association with each of the n MEMS sensors SE1 to SEn as a positional relationship ( Step S18
). FIG. 10 shows an example of the storage table 102 stored at a certain measurement time stored in the status memory 42.

In the above, the train platform approach detection unit 52 described above is realized in step S15, the train stop detection unit 53 described above is realized in step S16, and the train departure detection unit 54 described above is realized in step S17. Thus, in the status memory 42, the status information related to the train platform approach, the status information related to the stop of the train, and the status information related to the departure of the train are associated with the passage of time, and the n MEMS sensors SE1 to SEn. It is stored in a table format in association with each position of each detection area.

Next, on the condition that a train departure signal is generated, it is determined whether or not a passenger entrainment state has occurred.

In step S19, it is determined whether or not a departure signal for the train 12 has been generated. Step S1
If YES at 9, the process for detecting passenger entrainment is started at the next step S20. In this step S19, a process for synthesizing the first and second storage tables 101 and 102 stored in the two status memories 41 and 42 is performed to form a third storage table. The third storage table is stored in the status memory 43. In the third storage table stored in the status memory 43, n MEMS sensors SE1 to SE are used.
Information on the positional change relationship between the passenger image 25 and the train image is included in at least one detection image of n. For this reason, in the next step S21, the positional change relationship information between the passenger image and the train image is extracted based on the record information of the third storage table stored in the status memory 43.

In the next determination step S22, the extracted positional change relation information is compared with the above-described determination data 71 to determine whether or not they match. If NO in the determination step S22, it is determined that no passenger is involved, and the detection process is terminated. Y in determination step S22
If it is ES, an alarm signal and a stop signal are output to an external device (step S23). Thereafter, the detection process ends.

In the above, the passenger entrainment detection start unit 55 is realized by step S20, and the passenger entrainment determination unit 56 described above is realized by steps S21 and S22.

According to the above, in the home fall detection system, passengers are detected using the detected image information of the required number of MEMS sensors SE1, SE2,..., SEn arranged at a predetermined upper position corresponding to the home edge 11A of the home 11. Image and train image, and further information related to the relationship between the passenger image and the train image, and by comparing this with the judgment data (the relationship shown in FIG. 7), accurate and quick passenger entrainment Automatic detection is possible, and the occurrence of an accident can be prevented by performing an emergency safety operation.

As described above, the position and movement of the passenger in the danger area image 23 of the detection image 21 (
It is also possible to detect and determine passenger entrainment using only the information on (position change).

The configurations, shapes, sizes, and arrangement relationships described in the above embodiments are merely schematically shown to the extent that the present invention can be understood and implemented. Therefore, the present invention is not limited to the described embodiments, and can be variously modified without departing from the scope of the technical idea shown in the claims.

A passenger entrainment detection system at a home according to the present invention is configured such that a plurality of MEMS sensors are installed at a required arrangement position with respect to a detection area along a home edge of a home where a train of a station enters, and the ranging of the MEMS sensor is performed. The height information of the corresponding detection area is acquired using the function, and information related to the rotation operation of the passenger in the danger area at the time of departure of the train is obtained based on the height information to detect the passenger entrainment state. The passenger entrainment detection system at this home is a two-dimensional area sensor M
Since the object is detected based on the height information in the detection area using the EMS sensor, when an accident occurs in which the passenger is involved in the train along the edge of the platform on the platform, this is quickly detected with high accuracy. And it is used for surely detecting automatically.

DESCRIPTION OF SYMBOLS 11 Home 11A Home edge SE1-SEn MEMS sensor 12 Train 13 Mounting frame 14 Detection area 14A Scan profile 21 Detection image 22 Line image 23 Hazardous area image 24 Safety area image 25 Passenger image 31 Switch part 32 Calculation processing part 41,42,43 Status memory 51 Passenger tracking section 52 Train platform approach detection section 53 Train stop detection section 54 Train departure detection section 55 Passenger entrainment detection start section 56 Passenger entrainment detection section 61 Output device 71 Data for determination 71 Passenger 81-88 Image (MEMS sensor SE1 Detection image)
101 Storage table 102 Storage table

Claims (4)

Train is Installation along the station home edge of the incoming lines, for each meter measurement time, and at least one area type distance sensor for outputting a detection image of the detection area is set,
Said area type distance sensor is the detection to that Symbol憶means stores data related to passenger image based on the image to be output,
It determines that the obtains the motion data of passengers image based on data relating to prior Symbol passenger image, the passenger rolling state when the motion data as compared to the determination data indicates movement including rotation in hazardous areas has occurred Processing means;
A passenger entrainment detection system at home characterized by comprising: The storage means stores data relating to a train image based on the detection image output by the area type distance sensor ,
The processing means acquires positional relationship data between the passenger image and the train image based on movement data of the passenger image and data related to the train image, and compares the positional relationship data with the determination data. passenger entrainment detection system in claim 1 Symbol placement home and judging the passenger trapping condition occurs when indicating a motion including a rotation in hazardous areas the motion data to. Passenger entrainment detection system in claim 1 or 2, wherein the home and having each said boundary portion is being or overlapping portions, contact of the detection area of the two said area type distance sensor adjacent Ri each other.   The passenger at a home according to any one of claims 1 to 3, wherein the determination data is data relating to a passenger motion image in an area related to a dangerous area image in the detected image. Entrainment detection system.

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