Route environment monitoring system and route environment monitoring method

Abstract

To make it possible to detect abnormality and the like of route environment based on a camera image obtained by photographing a periphery of a train during the traveling of the train with the use of a camera installed for the purpose of detecting the pinching of a passenger or a stuff at a door.SOLUTION: A route environment monitoring system includes a camera 1 provided corresponding to a door for getting on / off of passengers of a train, an image analysis server 2 for detecting abnormality of route environment based on a camera image photographed by the camera and generating alarm information, and a notification device 3 and a monitoring terminal 6 acquiring the alarm information and notifying an attendant of an occurrence state of the abnormality in the route environment. The image analysis server detects the pinching of a passenger or a stuff at the door based on the camera image to generate alarm information about the pinching at the door in a first monitoring mode. The image analysis server detects the abnormality of the route environment based on the camera image to generate alarm information about the abnormality of the route environment in a second monitoring mode. The first monitoring mode and the second monitoring mode are switched by determining whether a train is in a stop state or a moving state.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a route environment monitoring system and a route environment monitoring method that detect abnormalities in the route environment of a moving body on which a person rides and notify the station staff of the abnormalities in the route environment.

Background Art

[0002] In the railway, it is desired to quickly detect abnormalities in the route environment that hinder the safe operation of trains and promptly carry out necessary measures such as track maintenance work.

[0003] As a technology for monitoring such a route environment, conventionally, a track inspection device equipped with a camera is mounted on a train operating in commercial service, the track on which the train runs is photographed by the camera, and abnormalities in the track are detected based on the photographed image (see Patent Document 1).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Now, in the conventional technology, by mounting a track inspection device on a train operating in commercial service, a dedicated inspection vehicle is not required, and even on a relatively long route, the route can be efficiently monitored. However, it costs a considerable amount to mount a track inspection device equipped with a camera on a train. Furthermore, in order to achieve monitoring with few omissions, it is necessary to mount track inspection devices on many trains operating in commercial service, resulting in an increase in cost.

[0006] On the one hand, when a train is stopped at a station platform, a passenger's body, belongings, etc. may be pinched in the door where passengers get on and off. Therefore, a technology that detects door pinching and notifies station staff or train crew that pinching has occurred is desired.

[0007] In recent years, as a technology for detecting such door pinching, a technology of installing a camera on the side surface of a train body and detecting door pinching based on a camera image captured by the camera has been spreading. Since such cameras for pinching monitoring are installed on all trains in commercial operation, if the cameras for pinching monitoring can be used for route monitoring purposes, an increase in cost can be suppressed.

[0008] Therefore, a main object of the present invention is to provide a route environment monitoring system and a route environment monitoring method that can detect abnormalities in the route environment, etc. based on a camera image obtained by photographing the periphery of a moving body during the running of the moving body, using a camera installed for the purpose of detecting door pinching.

Means for Solving the Problem

[0009] The route environment monitoring system of the present invention is a route environment monitoring system that detects an abnormality in the route environment of a moving body on which a person boards and generates reporting information including the abnormality detection result, and includes a camera provided corresponding to a door for a person to get on and off in the moving body, a server device that detects an abnormality in the route environment based on a camera image captured by the camera and generates the reporting information, and a terminal device that acquires the reporting information and notifies a station staff of the occurrence status of the abnormality in the route environment. The server device detects door pinching based on the camera image in a first monitoring mode and generates the reporting information regarding the door pinching, and detects an abnormality in the route environment based on the camera image in a second monitoring mode and generates the reporting information regarding the abnormality in the route environment, and is configured to switch between the first monitoring mode and the second monitoring mode by determining whether the moving body is in a stopped state or a moving state.

[0010] Further, a route environment monitoring method according to the present invention is a route environment monitoring method for causing an information processing device to perform a process of detecting an abnormality in a route environment of a moving body on which a person rides and generating notification information including the abnormality detection result, the method including: obtaining a camera image captured by a camera provided corresponding to a door for boarding and alighting of a person in the moving body; in a first monitoring mode, detecting pinching of the door based on the camera image and generating the notification information regarding the pinching of the door; in a second monitoring mode, detecting an abnormality in the route environment based on the camera image and generating the notification information regarding the abnormality in the route environment; and determining whether the moving body is in a stopped state or a moving state and switching between the first monitoring mode and the second monitoring mode.

Advantages of the Invention

[0011] According to the present invention, it is possible to detect an abnormality in a route or the like based on a camera image obtained by photographing the periphery of a moving body during travel of the moving body by using a camera installed for the purpose of detecting pinching of a door.

Brief Description of the Drawings

[0012]

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Mode for Carrying Out the Invention

[0013] The first invention made to solve the above problems is a route environment monitoring system that detects an abnormality in the route environment of a moving body on which a person rides and generates alarm information including the abnormality detection result. The system includes a camera provided corresponding to a door for a person to board and alight from the moving body, a server device that detects an abnormality in the route environment based on a camera image captured by the camera and generates the alarm information, and a terminal device that acquires the alarm information and notifies an attendant of the occurrence status of the abnormality in the route environment. The server device, in a first monitoring mode, detects pinching of the door based on the camera image and generates the alarm information regarding the pinching of the door, and in a second monitoring mode, detects an abnormality in the route environment based on the camera image and generates the alarm information regarding the abnormality in the route environment, and is configured to switch between the first monitoring mode and the second monitoring mode by determining whether the moving body is in a stopped state or a moving state.

[0014] According to this, it is possible to detect an abnormality in a route or the like based on a camera image obtained by photographing the periphery of a moving body during travel of the moving body, using a camera installed for the purpose of detecting pinching of a door.

[0015] Further, in the second invention, the server device integrates the abnormality detection results for each of the camera images taken by a plurality of the cameras provided in the same moving body, obtains the abnormality detection result for each moving body, and generates the reporting information including the abnormality detection result for each moving body.

[0016] According to this, it is possible to present to an attendant the occurrence status of an abnormality for each moving body (for example, a train).

[0017] Further, in the third invention, the server device integrates the abnormality detection results for each of the camera images taken by a plurality of the cameras provided for each of a plurality of components constituting the moving body, obtains the abnormality detection result for each component, and generates the reporting information including the abnormality detection result for each component.

[0018] According to this, it is possible to present to an attendant the occurrence status of an abnormality for each component (for example, a car body constituting a train) constituting the moving body.

[0019] Further, in the fourth invention, the server device integrates the abnormality detection results for each of a plurality of the moving bodies traveling on the same route, obtains the abnormality detection result for each route, and generates the reporting information including the abnormality detection result for each route.

[0020] According to this, it is possible to present to an attendant the occurrence status of an abnormality for each route.

[0021] Further, in the fifth invention, the server device obtains an abnormality level representing the degree of the abnormality for each detected abnormal event, and generates the reporting information including the abnormality level.

[0022] According to this, the degree of the detected abnormal event can be presented to the staff member.

[0023] Moreover, in the sixth invention, the server device is configured to distribute a plurality of monitoring screens including the reporting information to the terminal device.

[0024] According to this, the staff member can confirm the occurrence status of the abnormality in the route environment.

[0025] Moreover, in the seventh invention, one of the monitoring screens is configured to be a route map screen that presents the occurrence status of abnormalities in all the routes to be monitored to the staff member.

[0026] According to this, the occurrence status of abnormalities in all the routes to be monitored can be presented to the staff member by the route map screen.

[0027] Moreover, in the eighth invention, one of the monitoring screens is a route detail screen that presents the occurrence status of abnormalities in a predetermined route to the staff member, and is configured to be displayed when the staff member selects a route on another one of the monitoring screens.

[0028] According to this, the status of the abnormality that occurred in the designated route can be presented to the staff member by the route detail screen.

[0029] Moreover, in the ninth invention, one of the monitoring screens is an abnormal event confirmation screen for the staff member to confirm the status of the detected abnormal event by camera images, and is configured to be displayed when the staff member selects an abnormal event on another one of the monitoring screens.

[0030] According to this, the staff member can confirm the status of the detected abnormal event by camera images on the abnormal event confirmation screen.

[0031] Moreover, in the tenth invention, one of the monitoring screens is a time-series status screen that presents the temporal change status of the abnormalities detected by each moving body to the staff member, and is configured to be displayed when the staff member gives an instruction for time-series status display on another one of the monitoring screens.

[0032] According to this, the time-series situation screen can present to the staff the temporal change situation of the abnormalities detected by each moving body.

[0033] Also, in the 11th invention, one of the monitoring screens is a moving body situation screen that presents to the staff the positional change situation of the abnormalities detected by each moving body, and is configured to be displayed when the staff gives an instruction for moving body situation display on another said monitoring screen.

[0034] According to this, the moving body situation screen can present to the staff the positional change situation of the abnormalities detected by each moving body.

[0035] Also, in the 12th invention, one of the monitoring screens is a track detailed situation screen that presents to the staff the detailed situation of the abnormalities detected in each track section, and is configured to be displayed when the staff selects a track section on another said monitoring screen.

[0036] According to this, the track detailed situation screen can present to the staff the detailed situation of the abnormalities occurring in each track section.

[0037] Also, in the 13th invention, one of the monitoring screens is a moving body detailed situation screen that presents to the staff the detailed situation of the abnormalities detected by each moving body, and is configured to be displayed when the staff selects a moving body on another said monitoring screen.

[0038] According to this, the moving body detailed situation screen can present to the staff the detailed situation of the abnormalities occurring in each moving body.

[0039] Also, in the 14th invention, at least one of the monitoring screens is configured to include a synthetic aerial view image synthesized from the plurality of said camera images.

[0040] According to this, from the composite bird's-eye view image of the surroundings of the moving body as seen from above, the attendant can easily grasp the situation around the moving body, particularly the positional relationship between the location where an abnormality has occurred and the moving body.

[0041] Further, a 15th invention is a route environment monitoring method for causing an information processing device to perform a process of detecting an abnormality in a route environment of a moving body on which a person boards and generating notification information including the abnormality detection result. The method includes acquiring a camera image captured by a camera provided corresponding to a door for a person to board and alight from the moving body, and in a first monitoring mode, detecting pinching of the door based on the camera image and generating the notification information regarding the pinching of the door. In a second monitoring mode, detecting an abnormality in the route environment based on the camera image and generating the notification information regarding the abnormality in the route environment, and determining whether the moving body is in a stopped state or a moving state and switching between the first monitoring mode and the second monitoring mode.

[0042] According to this, similar to the first invention, by using a camera installed for the purpose of detecting pinching of the door and based on a camera image obtained by photographing the periphery of the moving body during the running of the moving body, an abnormality in the route or the like can be detected.

[0043] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0044] (First Embodiment) FIG. 1 is an overall configuration diagram of a route environment monitoring system according to the first embodiment.

[0045] The route environment monitoring system detects an abnormality in a route environment, that is, a track on which a train (moving body) runs and its surroundings, and notifies the occurrence of the abnormality to an attendant. The route environment monitoring system includes a camera 1, an image analysis server 2 (server device), a notification device 3 (terminal device), a radio base station 4, an analysis server 5 (server device), and a monitoring terminal 6 (terminal device).

[0046] The camera 1, the image analysis server 2, and the notification device 3 are connected via the network inside the train. The image analysis server 2 and the radio base station 4 are connected via the mobile communication network. The radio base station 4, the analysis server 5, and the monitoring terminal 6 are connected via networks such as a closed network or the Internet.

[0047] The camera 1 is installed on the side surface of the train body and photographs the outside of the door where passengers get on and off from the side.

[0048] The image analysis server 2 is installed on the train. The image analysis server 2 is composed of an information processing device such as a PC. Based on the camera images obtained from each camera 1, the image analysis server 2 detects the pinching of foreign objects (such as the bodies or belongings of passengers getting on and off) by the door. The detection result of this pinching detection process is transmitted to the notification device 3 as reporting information. Also, based on the camera images obtained from each camera 1, the image analysis server 2 detects abnormalities related to the track environment. The detection results (analysis results) of this track abnormality detection process and the camera images are stored in the device itself. Also, the camera images and analysis results are transmitted to the analysis server 5 as reporting information in real time or at an appropriate timing.

[0049] The notification device 3 is installed in the train crew's cabin (such as the conductor's cabin). When the image analysis server 2 detects pinching, the notification device 3 displays on the display device a monitoring screen indicating the position of the door where the pinching occurred, and notifies the train crew (such as the conductor) of the pinching of the door by lighting a lamp and sounding a buzzer, etc.

[0050] The analysis server 5 is installed in a monitoring center (such as an operation command post). The analysis server 5 collects the camera images and analysis results from the image analysis servers 2 installed on each train, and performs necessary processing on the camera images and analysis results to generate various monitoring screens for presenting the occurrence status of abnormalities related to the track environment to the staff, and distributes the monitoring screens to the monitoring terminal 6 as reporting information.

[0051] The monitoring terminal 6 is installed at a monitoring center or the like. The monitoring terminal 6 is viewed by the staff (commanders) of the monitoring center. A monitoring screen distributed from the analysis server 5 is displayed on the monitoring terminal 6.

[0052] In this embodiment, an example in which the moving body (vehicle carrying people) on which a person rides is a train will be described, but the moving body is not limited to a train. For example, it may be a moving body using a ropeway such as a ropeway or a lift. Also, it may be a moving body traveling on a road such as a bus.

[0053] Next, the camera image captured by the camera 1 will be described. FIG. 2 is an explanatory diagram showing the camera image.

[0054] The camera 1 is installed on the side surface of the train body and captures the outside of the door where passengers get on and off from the side. Therefore, when the train is stopped at the station platform, as shown in FIG. 2(A), the station platform and passengers are captured in the camera image captured by the camera 1. On the other hand, when the train is running on the track between stations, as shown in FIG. 2(B), the track (roadbed, subgrade) and overhead line poles and the like around it are captured in the camera image.

[0055] In this embodiment, the image analysis server 2 determines whether the train is in a stopped state (the state where the train is stopped at the platform) or a moving state. When the train is in a stopped state, it detects the pinching of foreign objects (such as the bodies and belongings of passengers getting on and off) by the door based on the camera image. When the train is in a moving state, it detects an abnormality in the track environment based on the camera image.

[0056] Note that the abnormality in the track environment, that is, the abnormality occurring in the track and its surrounding area, is, for example, the swaying of the vehicle body caused by rail irregularities, or the abnormal change in the color of the subgrade caused by the inflow of earth and sand, the overgrowth of vegetation, or the intrusion of other obstacles. Also, it may be configured to collect information on various events other than the abnormal events occurring in the track and its surrounding area. For example, it may be configured to collect information on damage to signs, signals, level crossings, platforms, etc., and snowfall conditions.

[0057] Next, the schematic configurations of the image analysis server 2 and the analysis server 5 will be described. FIG. 3 is a block diagram showing the schematic configurations of the image analysis server 2 and the analysis server 5.

[0058] The image analysis server 2 includes an in-train communication unit 21, an out-of-train communication unit 22, a storage unit 23, and a processor 24.

[0059] The in-train communication unit 21 communicates with the cameras 1 installed in each vehicle (carriage) of the train and the notification device 3 installed in the driver's cab.

[0060] The out-of-train communication unit 22 communicates with the analysis server 5 via the radio base station 4 and the network.

[0061] The storage unit 23 stores programs executed by the processor 24 and the like. Further, the storage unit 23 stores registration information of a database that manages camera images and their analysis results.

[0062] The processor 24 performs various processes by executing the programs stored in the storage unit 23. In the present embodiment, the processor 24 performs monitoring mode determination processing, pinching detection processing, route abnormality detection processing, and the like.

[0063] In the monitoring mode determination processing, the processor 24 determines whether the train is in a stopped state or a moving state based on the train speed and the like, and switches between a pinching monitoring mode (first monitoring mode) and a route environment monitoring mode (second monitoring mode) according to the determination result. Specifically, when the train is in a stopped state (when the train is stopped at a platform), the pinching monitoring mode is set, and when the train is in a moving state, the route environment monitoring mode is set. The pinching monitoring mode monitors for pinching of the door, and in this mode, pinching detection processing is performed. The route environment monitoring mode monitors for abnormalities along the line, and in this mode, route abnormality detection processing is performed.

[0064] In the pinching detection process, the processor 24 detects the pinching of foreign objects (such as the bodies or belongings of passengers getting on and off) by the door based on the camera images acquired from each camera 1. In the pinching detection process, an image recognition model (machine learning model) constructed by machine learning such as deep learning can be used. In this case, the camera image is input into the image recognition model, and the pinching detection result output from the image recognition model is acquired.

[0065] In the route abnormality detection process, the processor 24 detects an abnormality related to the route environment based on the camera images acquired from each camera 1. In addition to the abnormality related to the route environment, the processor 24 also detects an abnormality of the train itself (for example, an abnormal sway of the car body based on an unnatural sway image). In the route abnormality detection process, as the analysis result (the detection result of the route abnormality detection process), the level of the abnormality (warning, caution, good) and the content of the abnormality (abnormal sway of the car body, abnormal change in the color of the roadbed) are acquired. In the route abnormality detection process, an image recognition model (machine learning model) constructed by machine learning such as deep learning can be used. In this case, the camera image is input into the image recognition model, and the abnormality detection result output from the image recognition model is acquired.

[0066] Note that the image recognition model may be composed of three-class discriminators that identify three abnormality levels of warning, caution, and good. Also, the image recognition model may be composed of discriminators that respectively identify abnormalities in the roadbed, roadbed, overhead line poles, and outside the track.

[0067] In addition to this, the processor 24 performs an image synthesis process of synthesizing the original camera images to generate a synthesized bird's-eye view image. The synthesized bird's-eye view image is an image of the state of looking at the surroundings of the vehicle (car body) from above, and is generated by synthesizing the camera images captured by a plurality of cameras 1 installed on the vehicle. In the example shown in FIG. 1, four cameras 1 are installed on one car body, and a synthesized bird's-eye view image is generated by synthesizing the four camera images captured by the four cameras 1.

[0068] Note that the monitoring mode determination process and the pinching detection process are performed in real time, but the track abnormality detection process does not necessarily have to be performed in real time and may be performed at an appropriate timing.

[0069] The analysis server 5 includes a communication unit 51, a storage unit 52, and a processor 53.

[0070] The communication unit 51 communicates with the image analysis server 2 and the monitoring terminal 6 via a network.

[0071] The storage unit 52 stores programs executed by the processor 53 and the like.

[0072] The processor 53 performs various processes by executing the programs stored in the storage unit 52. In the present embodiment, the processor 53 performs analysis processing, screen generation processing, and the like.

[0073] In the analysis process, the processor 53 generates abnormality detection results for each of the train, the vehicles (cars) that make up the train, the track, and the track sections that make up the track, that is, information indicating the occurrence status of abnormalities, based on the camera images acquired from the image analysis server 2 and the analysis results thereof.

[0074] Specifically, the abnormality detection results for each vehicle are obtained by integrating the abnormality detection results for each camera image captured by the plurality of cameras 1 installed on each vehicle (car) of the train. Further, the abnormality detection results for each train can be obtained by integrating the abnormality detection results for each of the plurality of vehicles. Also, the abnormality detection results for each track are obtained by integrating the abnormality detection results for each of the plurality of trains running on the same track. Also, the abnormality detection results for each track section are obtained by integrating the abnormality detection results for each of the plurality of trains running on the same track section.

[0075] In the screen generation process, the processor 53 generates various monitoring screens for presenting the occurrence status of abnormalities to the staff based on the camera images and their analysis results obtained from the image analysis server 2 and the information obtained in the analysis process. Specifically, it generates a route map screen 101 (see FIG. 7), a route detail screen 111 (see FIG. 8), an abnormal event confirmation screen 121 (see FIG. 10), a time-series status screen 131 (see FIG. 11), a train status screen 141 (see FIG. 12), a track detail status screen 151 (see FIG. 13), and a train detail status screen 161 (see FIGS. 14 and 15). These monitoring screens are distributed to the monitoring terminal 6.

[0076] Next, the operation procedure of the image analysis server 2 will be described. FIG. 4 is a flowchart showing the operation procedure of the image analysis server 2.

[0077] In the image analysis server 2, first, the processor 24 sets it to the pinching monitoring mode (the first monitoring mode) (ST101). In the pinching monitoring mode, the pinching detection process using the camera image is performed. Next, the processor 24 determines whether the train has changed from the stopped state to the moving state based on the train speed (ST102).

[0078] Here, when the train changes from the stopped state to the moving state (Yes in ST102), the processor 24 sets it to the route environment monitoring mode (the second monitoring mode) (ST103). In the route environment monitoring mode, the route abnormality detection process using the camera image is performed. Next, the processor 24 determines whether the train has changed from the moving state to the stopped state based on the train speed (ST104).

[0079] Here, when the train changes from the moving state to the stopped state (Yes in ST104), it returns to ST101, and the processor 24 sets it to the pinching monitoring mode (the first monitoring mode). Thereafter, the same process is repeated.

[0080] Here, the train may stop on the track between stations due to a stop signal of a traffic signal or the like. Therefore, in the determination (ST104) of whether the train has changed from the moving state to the stopped state, it is preferable to determine whether the train has stopped at the platform. At this time, based on the door opening / closing signal, the camera image, or the like, it may be determined whether the train has stopped at the platform.

[0081] Next, the database managed by the image analysis server 2 will be described. FIG. 5 is an explanatory diagram showing the registration contents of the database managed by the image analysis server 2.

[0082] In the image analysis server 2, in the route environment monitoring mode (second monitoring mode), as a route abnormality detection process, image analysis for detecting an abnormality related to the route environment and an abnormality of the train itself is performed based on the camera images acquired from each camera 1, and the camera images and their analysis results (abnormality detection results by the route abnormality detection process) are registered in the database. In addition to the camera images and their analysis results, information such as the train number, car body number, date and time, and composite bird's-eye view image is registered in the database.

[0083] Here, in the route abnormality detection process, as the analysis result (detection result of the route abnormality detection process), the level of the abnormality (warning, caution, good) and the content of the abnormality (abnormal sway of the car body, abnormal change in the color of the roadbed) are acquired. When an image recognition model (machine learning model) is used for the route abnormality detection process, by inputting the camera image into the image recognition model, the level of the abnormality and the content of the abnormality are acquired as the detection results of the route abnormality output from the image recognition model.

[0084] The abnormality detection results (analysis results) by the route abnormality detection process are generated for each camera image at each time of each camera 1. On the other hand, on the monitoring screen distributed from the analysis server 5 to the monitoring terminal 6, when presenting the occurrence status of abnormalities for each vehicle (car body), for each train, for each route, or for each track section, it becomes easier for the staff to intuitively grasp the occurrence status of abnormalities.

[0085] Therefore, in the present embodiment, the analysis server 5 integrates the abnormality detection results for each camera image captured by a plurality of cameras 1 installed in each vehicle (carriage) of the train as an analysis process, thereby obtaining the abnormality detection result for each vehicle. Further, by integrating the abnormality detection results for a plurality of vehicles, the abnormality detection result for each train can be obtained. Also, by integrating the abnormality detection results for a plurality of trains running on the same route, the abnormality detection result for each route can be obtained. Further, by integrating the abnormality detection results for a plurality of trains running in the same track section, the abnormality detection result for each track section can be obtained.

[0086] As described above, in the present embodiment, the abnormality detection result for each target unit is obtained by integrating the abnormality detection results for each element constituting the target. At this time, the abnormality detection result with a higher abnormality level (warning, caution, normal) is prioritized. Specifically, if there is a warning in even one of the plurality of elements, the abnormality level of the target unit is set to warning. Also, if there is no warning among the plurality of elements and there is a caution in even one of the plurality of elements, the abnormality level of the target unit is set to caution. Further, if there is neither a warning nor a caution among the plurality of elements, the abnormality level of the target unit is set to normal.

[0087] For example, in the train detail status screen 161 (see FIGS. 14 and 15), the carriage button 162 is displayed in the color corresponding to the highest abnormality level among the abnormality levels for each camera image captured by the camera 1 installed in the corresponding vehicle. Also, in the route detail screen 111 (see FIG. 8), the train mark 115 is displayed in the color corresponding to the highest abnormality level among the abnormality levels for each vehicle (carriage) constituting the corresponding train. Further, in the route map screen 101 (see FIG. 7), the route on the route map is displayed in the color corresponding to the highest abnormality level among the abnormality levels for each train running on the corresponding route.

[0088] Next, various monitoring screens displayed on the monitoring terminal 6 will be described. FIG. 6 is an explanatory diagram showing the transition status of the monitoring screen.

[0089] When the monitoring terminal 6 accesses the analysis server 5, first, a route map screen 101 (see Fig. 7) is displayed. The route map screen 101 presents the occurrence status of abnormalities in all the routes to be monitored to the staff. When the staff operates the route button 105 on the route map screen 101 to select a route, the screen transitions to a route details screen 111 (see Fig. 8) related to the selected route.

[0090] The route details screen 111 (see Fig. 8) presents the status of abnormalities that have occurred on the selected route to the staff. When the staff operates the abnormal event button 117 on the route details screen 111 to select an abnormal event, the screen transitions to an abnormal event confirmation screen 121 (see Fig. 10) related to the selected abnormal event. Also, when the staff operates the time-series status display button 118 on the route details screen 111, the screen transitions to a time-series status screen 131 (see Fig. 11). Further, when the staff operates the train status display button 119 on the route details screen 111, the screen transitions to a train status screen 141 (see Fig. 12). Additionally, when the staff operates the train mark 115 on the route details screen 111 to select a train, the screen transitions to a train detailed status screen 161 (see Figs. 14 and 15) related to the selected train.

[0091] The abnormal event confirmation screen 121 (see Fig. 10) allows the staff to confirm the status of the detected abnormal event using camera images.

[0092] The time-series status screen 131 (see Fig. 11) presents the temporal change status of abnormalities detected by each train to the staff. When the staff operates the train button 134 on the time-series status screen 131 to select a train, the screen transitions to a train detailed status screen 161 (see Figs. 14 and 15) related to the selected train.

[0093] The train status screen 141 (see Fig. 12) presents the positional change status of abnormalities detected by each train to the staff. When the staff operates the track section button 144 on the train status screen 141 to select a track section, the screen transitions to a track detailed status screen 151 (see Fig. 13) related to the selected track section.

[0094] The track detailed status screen 151 (see FIG. 13) presents the detailed status of abnormalities that occurred in each track section to the operator. When the operator operates the abnormality event button 156 on the track detailed status screen 151 to select an abnormality event, the screen transitions to the train detailed status screen 161 (see FIGS. 14 and 15) related to the selected abnormality event.

[0095] The train detailed status screen 161 (see FIGS. 14 and 15) presents the detailed status of abnormalities that occurred in each train to the operator.

[0096] In addition, a return button 109 is provided on each screen except the route map screen 101 (initial screen). When the operator operates the return button 109, the screen returns to the previous screen. For example, when the operator operates the return button 109 on the route detailed screen 111 (see FIG. 8), the screen returns to the route map screen 101 (see FIG. 7), which is the previous screen.

[0097] Next, the route map screen 101 displayed on the monitoring terminal 6 will be described. FIG. 7 is an explanatory diagram showing the route map screen 101.

[0098] When the monitoring terminal 6 accesses the analysis server 5, first, the route map screen 101 is displayed. The route map screen 101 presents the occurrence status of abnormalities in all the routes to be monitored to the operator.

[0099] A route map 102 is displayed on the route map screen 101. On the route map 102, each route is drawn in a color corresponding to the abnormality level (warning, caution, good).

[0100] Also, on the route map screen 101, the current date and time are displayed on the date and time display section 103.

[0101] In addition, an information display section 104 for each route is provided on the route map screen 101. The information display section 104 displays the number of occurrences of abnormal events classified by abnormal level (warning, caution), and the number of unhandled abnormal events, that is, the number of abnormal events for which confirmation by the staff is outstanding.

[0102] In addition, a route button 105 is provided in the information display section 104. When the staff operates the route button 105 to select a route, the screen transitions to the route details screen 111 (see FIG. 8).

[0103] In addition, a history list display section 106 is provided on the route map screen 101. In the history list display section 106, information regarding each abnormal event detected in the past is displayed in a list. Here, as information regarding the abnormal event, the date and time, the route name, the train number, the camera number, and the analysis result are displayed. In the analysis result, the content of the abnormal event and the abnormal level are displayed. Thereby, the staff can confirm the abnormal events that have occurred in the past on all routes. Note that in the history list display section 106, the information regarding the latest abnormal event is displayed at the bottom.

[0104] Next, the route details screen 111 displayed on the monitoring terminal 6 will be described. FIG. 8 is an explanatory diagram showing the route details screen 111.

[0105] When the staff operates the route button 105 to select a route on the route map screen 101 (see FIG. 7), the screen transitions to the route details screen 111. The route details screen 111 presents the situation of the abnormalities that have occurred on the selected route to the staff.

[0106] A route details diagram 112 is displayed on the route details screen 111. The route details diagram 112 displays station marks 113, track section marks 114, and train marks 115.

[0107] The station mark 113 is drawn in a color according to whether the station can accept trains or not. In the analysis server 5, the congestion situation of the station is detected by using the captured images of the cameras 1 installed at the entrances and exits and platforms of the station, and whether the train can be accepted is determined based on the congestion situation.

[0108] The track section mark 114 is drawn in a color according to the abnormal level (warning, caution) of the abnormal event occurring in the track section. In the example shown in FIG. 8, an abnormality has occurred in the track section of #1202, and this track section becomes the target for treatment.

[0109] The train mark 115 is arranged at a position corresponding to the actual position of the train on the track and moves as the train progresses. The train mark 115 is drawn in a color according to the abnormal level (warning, caution). In the example shown in FIG. 8, an abnormality (warning) has occurred in the train of #113, and this train becomes the target for treatment. Also, the train mark 115 may perform a different type of highlighting when in a congested state than the abnormal level. In the example shown in FIG. 8, since the train of #113 is in a congested state, diagonal lines are drawn on the train mark 115. When an attendant operates the train mark 115 to select a train, it transitions to the train detailed status screen 161 (see FIGS. 14 and 15).

[0110] Also, on the route detailed map 112, the features along the line are drawn. In the example shown in FIG. 8, a tunnel and a slope are drawn.

[0111] Also, on the route detailed screen 111, a history list display section 116 is provided. In the history list display section 116, information about each abnormal event detected in the past is displayed in a list. Here, as information about the abnormal event, the date and time, the train number, the camera number, and the analysis result are displayed. In the analysis result, the content and the abnormal level of the abnormal event are displayed. Thereby, an attendant can confirm the abnormal events that have occurred in the past on the selected route. Note that in the history list display section 116, the information about the latest abnormal event is displayed at the bottom.

[0112] The history list display section 116 is provided with an abnormal event button 117 in the display column for each abnormal event. When an operator operates the abnormal event button 117 to select an abnormal event, the system transitions to the abnormal event confirmation screen 121 (see FIG. 10).

[0113] In addition, the route details screen 111 is provided with a time-series status display button 118 and a train status display button 119. When an operator operates the time-series status display button 118, the system transitions to the time-series status screen 131 (see FIG. 11). When an operator operates the train status display button 119, the system transitions to the train status screen 141 (see FIG. 12).

[0114] Here, when an operator (dispatcher) at the monitoring center (operation command post) finds a train with an abnormality on the route details screen 111 (see FIG. 8), the operator operates the abnormal event button 117 for the train of interest to display the abnormal event confirmation screen 121 (see FIG. 10) and confirm the abnormal event occurring on the train of interest with the camera image. Then, the operator gives necessary instructions to the driver of the train of interest.

[0115] At this time, the operator checks whether the station located in the traveling direction of the train of interest can accept the train of interest, that is, whether there is congestion at the station located in the traveling direction of the train of interest. Here, if the station located in the traveling direction of the train of interest can accept the train, the operator instructs the train of interest to proceed directly to enter the station. On the other hand, if the station located in the traveling direction of the train of interest cannot accept the train, the operator instructs the train of interest to wait in front of the station. In the example shown in FIG. 8, since an abnormality has occurred in train #113, this train becomes the target for treatment, and the operator checks whether A3 station, which is located in the traveling direction of train #113, can accept the train. In this case, since A3 station cannot accept the train, the operator instructs the driver to wait in front of A3 station.

[0116] Next, an overview of the route abnormality detection process performed by the image analysis server 2 will be described. FIG. 9 is an explanatory diagram showing an overview of the route abnormality detection process.

[0117] In the image analysis server 2, a track abnormality detection process is performed. In the track abnormality detection process, based on the camera image, an abnormality in the track environment, that is, an abnormality occurring in the track on which the train travels and its surroundings is detected. Further, in the present embodiment, an abnormality occurring in the train itself is detected. Then, based on the detection result regarding the abnormality in the track environment and the detection result regarding the abnormality in the train itself, the status of the track section and the train is displayed on a monitoring screen such as the track detail screen 111 (see FIG. 8).

[0118] Here, in the present embodiment, based on the occurrence status of abnormalities for each of a plurality of trains that have traveled on one track, it is determined whether the abnormality is in the track environment or in the train itself. Specifically, when an abnormality is detected in one of the plurality of trains traveling on one track, it is determined that there is an abnormality in the train. On the other hand, when an abnormality is detected in all of the plurality of trains traveling on one track, it is determined that there is an abnormality in the track environment.

[0119] In the example shown in FIGS. 9(A-1), (A-2), and (A-3), when each of the trains #114, #113, and #112 sequentially passes through the track sections #1104, #1105, and #1106, an abnormality (for example, swaying of the car body) is detected only in the train #113, and no abnormality is detected in each of the trains #114 and #112. In this case, it is determined that there is an abnormality in the train #113 itself.

[0120] In the example shown in FIGS. 9(B-1), (B-2), and (B-3), when each of the trains #122, #123, and #124 sequentially passes through the track sections #1203, #1202, and #1201, an abnormality (for example, swaying of the car body) is detected in all of the trains #122, #123, and #124 when passing through the track section #1202. In this case, it is determined that there is an abnormality in the track section #1202.

[0121] Next, the abnormality event confirmation screen 121 displayed on the monitoring terminal 6 will be described. FIG. 10 is an explanatory diagram showing the abnormality event confirmation screen 121.

[0122] When an attendant operates the abnormal event button 117 to select an abnormal event on the route details screen 111 (see Fig. 8), the screen transitions to the abnormal event confirmation screen 121. The abnormal event confirmation screen 121 is for the attendant (monitor) to confirm the situation of the detected abnormal event with the camera image. When the attendant confirms the situation of the abnormal event on the abnormal event confirmation screen 121, the attendant takes appropriate measures according to the abnormal event. Specifically, the attendant notifies the driver that an abnormality has occurred and gives necessary instructions to the driver. For example, the attendant instructs the driver to stop promptly at the nearest station.

[0123] On the abnormal event confirmation screen 121, first and second camera image display sections 122 and 123 are provided. In the first camera image display section 122, a camera image (still image) at the time when the abnormal event is detected is displayed. In the second camera image display section 123, camera images (moving images) for a predetermined period (for example, 10 seconds) before and after the time when the abnormal event is detected are displayed. Thereby, the attendant can confirm the occurrence situation of the abnormality, particularly the situation at the time when the abnormal event is detected and the situations before and after it.

[0124] Also, on the abnormal event confirmation screen 121, an instruction content input field 124 is provided. In the instruction content input field 124, the attendant can input in characters the content of the instructions given to the relevant persons (such as the driver) regarding the occurred abnormal event.

[0125] Also, on the abnormal event confirmation screen 121, a confirmation completion button 125 is provided. The attendant confirms the situation of the abnormal event with the camera images in the first and second camera image display sections 122 and 123, inputs the content of the instructions given to the relevant persons in the instruction content input field 124 if necessary, and then operates the confirmation completion button 125. Thereby, in the analysis server 5, information regarding the instruction content input in the instruction content input field 124 is accumulated together with information indicating that the attendant has confirmed it regarding the abnormal event and information regarding the date and time confirmed by the attendant.

[0126] Next, the time-series situation screen 131 displayed on the monitoring terminal 6 will be described. Fig. 11 is an explanatory diagram showing the time-series situation screen 131.

[0127] When an operator operates the time-series status display button 118 on the route details screen 111 (see FIG. 8), the screen transitions to the time-series status screen 131. The time-series status screen 131 presents to the operator the temporal change status of the abnormalities detected in each train.

[0128] The time-series status screen 131 is provided with a time-band display section 132. In the time-band display section 132, an alert mark 133 indicating the occurrence of an abnormality is displayed for each time band in each train. Thereby, the operator can grasp the occurrence status of abnormalities for each time band in each train. Further, the alert mark 133 is displayed in a manner that allows the abnormality level (warning, caution) to be distinguishable by color and characters. Thereby, the operator can grasp the abnormality level of the occurred abnormality.

[0129] In the example shown in FIG. 11, in the train #113, an abnormality of the warning level occurred in the time band of 10:00, and an abnormality of the caution level continued to occur in the time bands after 11:00. Thereby, it is presumed that an abnormality has occurred in the train #113 itself.

[0130] A train button 134 is provided in the display column for each train in the time-band display section 132. When the operator operates the train button 134 to select a train, the screen transitions to the train detailed status screen 161 (see FIGS. 14 and 15). At this time, information regarding the selected train is displayed on the train detailed status screen 161.

[0131] Further, the time-series status screen 131 is provided with a graph display section 135. A graph representing the number of abnormality occurrences for each train is displayed in the graph display section 135. The number of abnormality occurrences is the cumulative value for a predetermined past period (for example, the most recent one week). Thereby, the operator can grasp the occurrence status of abnormalities for each train in the predetermined past period.

[0132] Next, the train status screen 141 displayed on the monitoring terminal 6 will be described. FIG. 12 is an explanatory diagram showing the train status screen 141.

[0133] When an attendant operates the train status display button 119 on the route details screen 111 (see Fig. 8), the screen changes to the train status screen 141. The train status screen 141 presents to the attendant the positional change status of the abnormalities detected in each train.

[0134] On the train status screen 141, a display section 142 for each track section is provided. In the display section 142 for each track section, an alert mark 143 indicating the occurrence of an abnormality is displayed for each train in each track section. This enables the attendant to grasp the occurrence status of abnormalities in each track section of each train. Also, the alert mark 143 is displayed in a manner that allows the abnormality level (warning, caution) to be distinguishable by color and text. This enables the attendant to grasp the abnormality level of the occurred abnormality.

[0135] In the example shown in Fig. 12, in the train #113, an abnormality at the warning level occurred in the track section #1101, and an abnormality at the caution level continued to occur in the track sections after #1102. Thus, it is presumed that an abnormality has occurred in the train #113 itself. Also, in the track section #1202, an abnormality at the warning level has occurred in all of the trains #122, #123, and #124. Thus, it is presumed that an abnormality has occurred in the track section #1202.

[0136] In the display column for each track section of the display section 142 for each track section, a track section button 144 is provided. When the attendant operates the track section button 144 to select a track section, the screen changes to the track detailed status screen 151 (see Fig. 13). At this time, information regarding the selected track section is displayed on the track detailed status screen 151.

[0137] Also, on the train status screen 141, a graph display section 145 is provided. In the graph display section 145, a graph representing the number of occurrences of abnormalities for each track section is displayed. The number of occurrences of abnormalities is the cumulative value for a past predetermined period (for example, the most recent one week). This enables the attendant to grasp the occurrence status of abnormalities for each track section in the past predetermined period.

[0138] Next, the track detailed status screen 151 displayed on the monitoring terminal 6 will be described. FIG. 13 is an explanatory diagram showing the track detailed status screen 151.

[0139] When an attendant operates the track section button 144 to select a track section on the train status screen 141 (see FIG. 12), the screen changes to the track detailed status screen 151. The track detailed status screen 151 presents the attendant with the detailed status of abnormalities that occurred in each track section.

[0140] On the track detailed status screen 151, track section buttons 152 (tabs) are provided for each track section. The attendant can select a track section by operating the track section button 152. Also, the track section button 152 is displayed in a color corresponding to the abnormality level when an abnormality occurs.

[0141] In addition, the track detailed status screen 151 is provided with first and second camera image display sections 153 and 154. The first camera image display section 153 displays a camera image (still image) at the time when an abnormal event is detected. The second camera image display section 154 displays camera images (moving images) for a predetermined period (e.g., 10 seconds) before and after the time when an abnormal event is detected. When the attendant operates the track section button 152 to select a track section, the camera images related to the selected track section are displayed on the first and second camera image display sections 153 and 154. Thereby, the attendant can visually check the camera images in the selected track section to confirm the occurrence status of the abnormality, particularly the status at the time when the abnormal event was detected and the status before and after that time.

[0142] In addition, the track detail status screen 151 is provided with a history list display section 155. In the history list display section 155, information regarding each detected abnormal event in the past is displayed in a list. Here, as information regarding the abnormal event, the date and time, the track section number, the train number, the camera number, and the analysis result are displayed. In the analysis result, the content and the abnormal level of the abnormal event are displayed. When an operator operates the track section button 152 to select a track section, information on past abnormal events regarding the selected track section is displayed in the history list display section 155. Thereby, the operator can confirm abnormal events that have occurred in the past in the selected track section. Note that in the history list display section 155, information regarding the latest abnormal event is displayed at the bottom most.

[0143] An abnormal event button 156 is provided in the display column for each abnormal event in the history list display section 155. When an operator operates the abnormal event button 156 to select an abnormal event, the screen transitions to the train detail status screen 161 (see FIGS. 14 and 15).

[0144] Next, the train detail status screen 161 displayed on the monitoring terminal 6 will be described. FIG. 14 is an explanatory diagram showing the train detail status screen 161 when an original image is selected. FIG. 15 is an explanatory diagram showing the train detail status screen 161 when a composite image is selected.

[0145] When an operator operates the abnormal event button 156 to select an abnormal event on the track detail status screen 151 (see FIG. 13), the screen transitions to the train detail status screen 161. The train detail status screen 161 presents the detailed status of abnormalities that have occurred in each train to the operator.

[0146] The train detail status screen 161 is provided with car body buttons 162 (tabs) for each car body (vehicle). The operator can select a car body by operating the car body button 162. In addition, the car body button 162 is displayed in a color corresponding to the abnormal level when an abnormality occurs.

[0147] In addition, on the train detailed status screen 161, a camera button 163 is provided for each camera 1. The example shown in FIGS. 14 and 15 is the case where four cameras 1 are installed in one car, and four camera buttons 163 are provided. The attendant can select the camera 1 by operating the camera button 163. Also, the camera button 163 is displayed in a color corresponding to the abnormal level when an abnormality occurs.

[0148] In addition, on the train detailed status screen 161, an image type button 164 (radio button) for selecting the type of image is provided. The attendant can select either the original image or the composite image by operating the image type button 164.

[0149] In addition, on the train detailed status screen 161, first and second camera image display units 165 and 166 are provided. The first camera image display unit 165 displays a camera image (still image) at the time when an abnormal event is detected. The second camera image display unit 166 displays camera images (moving images) for a predetermined period (for example, 10 seconds) before and after the time when an abnormal event is detected. When the attendant operates the car body button 162 to select a car body and operates the camera button 163 to select the camera 1, the camera image taken by the selected camera 1 in the selected car body is displayed on the first and second camera image display units 165 and 166. Thereby, the attendant can visually check the camera image taken by the selected car body and camera 1 to confirm the occurrence status of the abnormality, particularly the status at the time when the abnormal event is detected and the status before and after that.

[0150] Here, the example shown in FIG. 14 is the case where the original image is selected by operating the image type button 164. In this case, the unprocessed camera images are displayed on the first and second camera image display units 165 and 166. On the other hand, the example shown in FIG. 15 is the case where the composite image is selected by operating the image type button 164. In this case, the composite bird's-eye view image is displayed on the first and second camera image display units 165 and 166. The composite bird's-eye view image is an image of the surroundings of the vehicle (the numbered vehicle) seen from above, and is generated by synthesizing four camera images taken by a plurality (here, four) of cameras 1 installed on the vehicle. With this composite bird's-eye view image, the attendant can easily grasp the situation around the vehicle, particularly the positional relationship between the location where an abnormality has occurred and the vehicle.

[0151] In addition, a history list display unit 167 is provided on the train detailed status screen 161. In the history list display unit 167, information regarding each abnormal event detected in the past is displayed in a list. Here, as information regarding the abnormal event, the date and time, the train number, the camera number, and the analysis result are displayed. In the analysis result, the content of the abnormal event and the abnormal level are displayed. When the attendant operates the numbered vehicle button 162 to select a numbered vehicle (the vehicle), the information on the past abnormal events regarding the selected numbered vehicle is displayed in the history list display unit 167. Thereby, the attendant can confirm the abnormal events that have occurred in the past in the selected vehicle. Note that in the history list display unit 167, the information regarding the latest abnormal event is displayed at the bottom.

[0152] (Modification Example of the First Embodiment) Next, a modification example of the first embodiment will be described. Note that points not particularly mentioned here are the same as those in the above-described embodiment. FIG. 16 is an overall configuration diagram of a route environment monitoring system according to a modification example of the first embodiment.

[0153] In the first embodiment, Camera 1 was installed on the train. However, in this embodiment, in addition to Camera 1, Microphone 7 is installed on the train. Microphone 7 can pick up abnormal sounds generated during the train's operation. Also, Microphone 7 is installed for each Camera 1. Thereby, the abnormal sound picked up by Microphone 7 can be associated with the camera image to detect abnormalities in the track environment.

[0154] (Second Embodiment) Next, the second embodiment will be described. Note that points not particularly mentioned here are the same as those in the above embodiment. FIG. 17 is an overall configuration diagram of the track environment monitoring system according to the second embodiment.

[0155] In the first embodiment, the image analysis server 2 was installed on the train. However, in this embodiment, the image analysis server 9 is installed in the monitoring center, and instead, the image storage server 8 is installed on the train.

[0156] The image storage server 8 stores the camera images acquired from Camera 1 and provides the camera images to the image analysis server 9. The image analysis server 9 collects the camera images from the image storage servers 8 installed on each train, performs image analysis for anomaly detection on the camera images, stores the camera images and their analysis results, and transmits the camera images and their analysis results to the analysis server 5. The analysis server 5 acquires the camera images and their analysis results from the image analysis server 2, performs necessary processing on the camera images and their analysis results, generates various monitoring screens for presenting the occurrence status of anomalies to the staff, and distributes them to the monitoring terminal 6.

[0157] Note that a server device having the functions of both the image analysis server 9 and the analysis server 5 may be provided.

[0158] As described above, embodiments have been described as examples of the technology disclosed in the present application. However, the technology in the present disclosure is not limited thereto, and can also be applied to embodiments with changes, replacements, additions, omissions, etc. Further, it is also possible to form a new embodiment by combining the respective components described in the above embodiments.

Industrial Applicability

[0159] The route environment monitoring system and the route environment monitoring method according to the present invention have the effect of being able to detect abnormalities in a route based on a camera image obtained by photographing the periphery of a moving body during the running of the moving body by using a camera installed for the purpose of detecting door pinching, and are useful as a route environment monitoring system and a route environment monitoring method for detecting an abnormality in the route environment of a moving body in which a person rides and notifying the attendant of the abnormality in the route environment.

Explanation of Signs

[0160] 1 Camera 2 Image analysis server (server device) 3 Notification device (terminal device) 4 Wireless base station 5 Analysis server (server device) 6 Monitoring terminal (terminal device) 7 Microphone 8 Image storage server 9 Image analysis server 101 Route map screen 111 Route details screen 121 Abnormal event confirmation screen 131 Time series status screen 141 Train status screen 151 Track details status screen 161 Train details status screen

Claims

1. A route environment monitoring system that detects an abnormality in the route environment of a moving body on which a person boards and generates reporting information including the abnormality detection result, a camera provided corresponding to a door for a person to board and alight on the moving body, a server device that detects an abnormality in the route environment based on a camera image captured by the camera and generates the reporting information, and a terminal device that acquires the reporting information and notifies a staff member of the occurrence status of the abnormality in the route environment, wherein the server device in a first monitoring mode, detects pinching of the door based on the camera image and generates the reporting information regarding the pinching of the door, in a second monitoring mode, detects an abnormality in the route environment based on the camera image and generates the reporting information regarding the abnormality in the route environment, and discriminates whether the moving body is in a stopped state or a moving state, and switches between the first monitoring mode and the second monitoring mode. A route environment monitoring system characterized by this.

2. wherein the server device integrates the abnormality detection results for each camera image captured by a plurality of the cameras provided on the same moving body, obtains the abnormality detection result for each moving body, and generates the reporting information including the abnormality detection result for each moving body. The route environment monitoring system according to claim 1, characterized by this.

3. wherein the server device integrates the abnormality detection results for each camera image captured by a plurality of the cameras provided for each of a plurality of components constituting the moving body, obtains the abnormality detection result for each component, and generates the reporting information including the abnormality detection result for each component. The route environment monitoring system according to claim 1, characterized by this.

4. wherein the server device Integrate the abnormality detection results for each of the plurality of moving bodies traveling on the same route to obtain the abnormality detection result for each route. The route environment monitoring system according to claim 1, characterized in that the reporting information including the abnormality detection result for each route is generated.

5. The server device For each detected abnormal event, obtain an abnormality level representing the degree of the abnormality. The route environment monitoring system according to claim 1, characterized in that the reporting information including the abnormality level is generated.

6. The server device The route environment monitoring system according to claim 1, characterized in that a plurality of monitoring screens including the reporting information are distributed to the terminal device.

7. One of the monitoring screens is a route map screen that presents the occurrence status of abnormalities on all routes to be monitored to the staff, and is characterized by the route environment monitoring system according to claim 6.

8. One of the monitoring screens is a route detail screen that presents the occurrence status of abnormalities on a predetermined route to the staff, and is displayed when the staff selects a route on another monitoring screen, and is characterized by the route environment monitoring system according to claim 6.

9. One of the monitoring screens is an abnormal event confirmation screen for the staff to confirm the status of the detected abnormal event by a camera image, and is displayed when the staff selects an abnormal event on another monitoring screen, and is characterized by the route environment monitoring system according to claim 6.

10. One of the monitoring screens is a time-series status screen that presents the temporal change status of abnormalities detected by each moving body to the staff, and is displayed when the staff gives an instruction for time-series status display on another monitoring screen, and is characterized by the route environment monitoring system according to claim 6.

11. One of the monitoring screens is a mobile body status screen that presents to the operator the positional change status of the abnormalities detected by each mobile body, and is characterized in that it is displayed when the operator gives an instruction for mobile body status display on another one of the monitoring screens. The route environment monitoring system according to claim 6.

12. One of the monitoring screens is a track detailed status screen that presents to the operator the detailed status of the abnormalities detected in each track section, and is characterized in that it is displayed when the operator selects a track section on another one of the monitoring screens. The route environment monitoring system according to claim 6.

13. One of the monitoring screens is a mobile body detailed status screen that presents to the operator the detailed status of the abnormalities detected by each mobile body, and is characterized in that it is displayed when the operator selects a mobile body on another one of the monitoring screens. The route environment monitoring system according to claim 6.

14. At least one of the monitoring screens includes a composite bird's-eye view image synthesized from a plurality of the camera images. The route environment monitoring system according to claim 6.

15. A route environment monitoring method for causing an information processing device to perform a process of detecting an abnormality in the route environment of a mobile body on which a person boards and generating reporting information including the abnormality detection result, acquiring a camera image captured by a camera provided corresponding to a door for boarding and alighting of a person in the mobile body, In a first monitoring mode, detecting pinching of the door based on the camera image and generating the reporting information regarding the pinching of the door, In a second monitoring mode, detecting an abnormality in the route environment based on the camera image and generating the reporting information regarding the abnormality in the route environment, characterized by determining whether the mobile body is in a stopped state or a moving state and switching between the first monitoring mode and the second monitoring mode.

Images