CN113256924A - Monitoring system, monitoring method and monitoring device for rail train - Google Patents
Monitoring system, monitoring method and monitoring device for rail train Download PDFInfo
- Publication number
- CN113256924A CN113256924A CN202010089249.XA CN202010089249A CN113256924A CN 113256924 A CN113256924 A CN 113256924A CN 202010089249 A CN202010089249 A CN 202010089249A CN 113256924 A CN113256924 A CN 113256924A
- Authority
- CN
- China
- Prior art keywords
- early warning
- monitoring
- preset
- analysis
- rail train
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 219
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000012806 monitoring device Methods 0.000 title claims abstract description 13
- 230000006399 behavior Effects 0.000 claims description 108
- 230000009471 action Effects 0.000 claims description 39
- 230000002159 abnormal effect Effects 0.000 claims description 15
- 230000009545 invasion Effects 0.000 claims description 6
- 238000009434 installation Methods 0.000 claims 1
- 238000001514 detection method Methods 0.000 description 12
- 230000004048 modification Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000033001 locomotion Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000011410 subtraction method Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0072—On-board train data handling
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0081—On-board diagnosis or maintenance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
- B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
- B61L23/041—Obstacle detection
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
- G08B13/19608—Tracking movement of a target, e.g. by detecting an object predefined as a target, using target direction and or velocity to predict its new position
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
- G08B13/19613—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
- G08B13/19613—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
- G08B13/19615—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion wherein said pattern is defined by the user
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19639—Details of the system layout
- G08B13/19647—Systems specially adapted for intrusion detection in or around a vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Mechanical Engineering (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Alarm Systems (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Image Analysis (AREA)
Abstract
The embodiment of the application provides a monitoring system, a monitoring method and a monitoring device for a rail train. A monitoring system, comprising: the acquisition device is used for acquiring monitoring data in the rail train, and the monitoring data comprises videos; the monitoring server is connected with the acquisition device to receive and store the monitoring data and transmit the monitoring data to each analysis host; the analysis hosts are respectively arranged in the carriages of the rail train and used for identifying and analyzing preset targets from the monitoring data and sending early warning information to an early warning device when the behavior of the preset targets meets preset early warning conditions; and the early warning device is connected with the analysis host and used for early warning after receiving the early warning information. The embodiment of the application solves the technical problems that the monitoring system of the existing rail train only collects monitoring data and does not analyze the monitoring data.
Description
Technical Field
The present disclosure relates to the field of rail train technologies, and in particular, to a monitoring system, a monitoring method, and a monitoring device for a rail train.
Background
At present, a carriage video monitoring system of a high-speed rail project is only provided with a passenger room camera and used for collecting monitoring data, including video and audio, and the monitoring data are stored in a video monitoring server, so that a driver can monitor the passenger room, and cannot analyze the data and warn emergency situations. .
Therefore, the existing monitoring system for the rail train only collects monitoring data, does not analyze the monitoring data, and is a technical problem which needs to be solved urgently by technical personnel in the field.
The above information disclosed in the background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not form the prior art that is known to those of ordinary skill in the art.
Disclosure of Invention
The embodiment of the application provides a monitoring system, a monitoring method and a monitoring device of a rail train, and aims to solve the technical problem that the existing monitoring system of the rail train only collects monitoring data and does not analyze the monitoring data.
The embodiment of the application provides a rail train's monitored control system includes:
the acquisition device is used for acquiring monitoring data in the rail train, and the monitoring data comprises videos;
the monitoring server is connected with the acquisition device to receive and store the monitoring data and transmit the monitoring data to each analysis host;
the analysis hosts are respectively arranged in the carriages of the rail train and used for identifying and analyzing preset targets from the monitoring data and sending early warning information to an early warning device when the behavior of the preset targets meets preset early warning conditions;
and the early warning device is connected with the monitoring server and is used for carrying out early warning after receiving the early warning information forwarded by the monitoring server.
The embodiment of the application also provides the following technical scheme:
a rail train monitoring method comprises the following steps:
acquiring monitoring data in the rail train, wherein the monitoring data comprises videos;
receiving and storing the monitoring data, and transmitting the monitoring data;
identifying and analyzing a preset target from the monitoring data, and sending early warning information when the behavior of the preset target meets a preset early warning condition;
and receiving early warning information and carrying out early warning.
The embodiment of the application also provides the following technical scheme:
a rail train monitoring device comprising:
the acquisition module is used for acquiring monitoring data in the rail train, wherein the monitoring data comprises videos;
the receiving and storing module is used for receiving and storing the monitoring data and transmitting the monitoring data;
the analysis module is used for identifying and analyzing a preset target from the monitoring data, and sending early warning information when the behavior of the preset target meets a preset early warning condition;
and the early warning module is used for receiving the early warning information and carrying out early warning.
Due to the adoption of the technical scheme, the embodiment of the application has the following technical effects:
the monitoring data in the rail train is acquired through the acquisition device and transmitted to the analysis hosts, and the analysis hosts are arranged in the carriage of the rail train, so that the analysis hosts analyze a large amount of monitoring data respectively, and the analysis speed can be increased; and identifying and analyzing a preset target from the monitoring data at the analysis host, and sending early warning information to an early warning device when the behavior of the preset target meets a preset early warning condition, so that the early warning device gives an early warning. The monitoring system of the rail train stores the monitoring data, facilitates later calling and checking, analyzes the monitoring data, performs early warning when preset early warning conditions are met, achieves automatic analysis and early warning, and provides basis for timely finding out special conditions and performing intervention measures as early as possible.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic structural diagram of a monitoring system of a rail train according to an embodiment of the present application;
FIG. 2 is a schematic view of the monitoring system of FIG. 1 and mounted to a rail train;
FIG. 3 is a flow chart of an analysis host of the monitoring system shown in FIG. 1 for performing an analysis of whether dangerous behavior exists;
FIG. 4 is a flowchart of an analysis host of the monitoring system shown in FIG. 1 for analyzing whether a protected area is invaded;
fig. 5 is a flowchart of the analysis of whether or not the person is crowded by the analysis host of the monitoring system shown in fig. 1.
Description of reference numerals:
100 acquisition devices, 110 hemispherical cameras, 120 panoramic cameras,
200 monitoring server, 300 analyzing host, 400 early warning device.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example one
Fig. 1 is a schematic structural diagram of a monitoring system of a rail train according to an embodiment of the present application; fig. 2 is a schematic view of the monitoring system shown in fig. 1 and a railway train.
As shown in fig. 1 and 2, a monitoring system for a rail train according to an embodiment of the present application includes:
an obtaining device 100, configured to obtain monitoring data in the rail train, where the monitoring data includes a video;
the monitoring server 200 is connected with the acquisition device to receive and store the monitoring data and transmit the monitoring data to the analysis host;
a plurality of analysis hosts 300, respectively disposed in the carriages of the rail train, for identifying and analyzing a preset target from the monitoring data, and sending an early warning message to an early warning device when a behavior of the preset target meets a preset early warning condition;
and the early warning device 400 is connected with the monitoring server and is used for carrying out early warning after receiving the early warning information forwarded by the monitoring server.
According to the monitoring system of the rail train, the monitoring data in the rail train are obtained through the obtaining device and transmitted to the analysis hosts, and the analysis hosts are arranged in the carriages of the rail train, so that the analysis hosts analyze a large amount of monitoring data respectively, and the analysis speed can be increased; and identifying and analyzing a preset target from the monitoring data at the analysis host, and sending early warning information to an early warning device when the behavior of the preset target meets a preset early warning condition, so that the early warning device gives an early warning. The monitoring system of the rail train stores the monitoring data, facilitates later calling and checking, analyzes the monitoring data, performs early warning when preset early warning conditions are met, achieves automatic analysis and early warning, and provides basis for timely finding out special conditions and performing intervention measures as early as possible.
In practice, ethernet connection is mostly adopted in rail trains. According to the rail train monitoring system, the acquisition device and the monitoring server are connected through the Ethernet, on one hand, the Ethernet of the rail train is effectively utilized, on the other hand, the monitoring data volume is large, and the monitoring system is also suitable for data transmission through the Ethernet. The monitoring server and the analysis host are connected through Ethernet.
The analysis host computer adopts the time difference principle and the background image difference hybrid algorithm to analyze the video, and extracts the information of the movement of the foreground target by subtracting the adjacent frame images. Procedure for video analysis (background subtraction method): firstly, the system carries out background learning, the learning time is different according to the background alarm degree, and the system automatically establishes a background model in the period. And then the system enters an analysis state, if a foreground has a moving object and is in a set range area and the size of a preset target meets the setting, the system extracts and tracks the preset target and triggers early warning according to a preset algorithm (intrusion, leaving behind, fighting and the like). (during which the monitoring system will initiate a pre-processing function to filter out these dynamic backgrounds if the background is jittered by the acquisition device, etc.). Before the early warning is triggered, the monitoring system has a function of recognizing a preset target, namely, the extracted preset target is compared with the established model, and the optimal matching is selected.
How the monitoring system achieves early warning of dangerous behaviors is explained below.
Fig. 3 is a flowchart of an analysis host of the monitoring system shown in fig. 1 for analyzing whether dangerous behaviors exist. In implementation, the preset early warning condition includes that an action amplitude threshold value is reached or exceeded;
as shown in fig. 3, the analysis host is specifically configured to analyze whether there is a dangerous behavior, and includes step S300: recognizing and analyzing the preset target from a dangerous behavior monitoring range preset in the video of the railway train passenger room area, and judging whether the behavior action amplitude of the preset target reaches or exceeds an action amplitude threshold value in the dangerous behavior monitoring range:
step S310: when the action amplitude threshold value is reached or exceeded, sending early warning information of dangerous behaviors to the early warning device;
step S320: when the action amplitude of the preset target does not reach or exceed the action amplitude threshold value within the dangerous action monitoring range, not sending early warning information of dangerous actions;
the early warning device is specifically used for carrying out early warning on dangerous behaviors according to the early warning information of the dangerous behaviors.
And analyzing whether dangerous behaviors exist or not according to a preset dangerous behavior monitoring range in the video of the railway train passenger room area, wherein the analysis is based on reaching or exceeding an action amplitude threshold value. When the action amplitude of the action of a person serving as a preset target reaches or exceeds an action amplitude threshold value within a preset dangerous action monitoring range, judging that dangerous action exists by the analysis host, and early warning; and when the action amplitude of the person as the preset target does not reach or exceed the action amplitude threshold value, the analysis host judges that no dangerous action exists. Dangerous behaviors can be set according to conditions, and various dangerous behaviors such as fighting and the like which need manual intervention by staff of the rail train are included through setting, so that early warning of the dangerous behaviors is realized.
And setting a preset dangerous behavior monitoring range according to the actions required by passengers in the passenger room area of the railway train. If the preset dangerous behavior monitoring range does not include the position where the passenger needs to take and place the luggage and other actions with large amplitude, otherwise misjudgment is easily caused. Specifically, a dangerous behavior monitoring range is set by setting a range of any polygon, and an audio detection mode, a video detection mode and an audio/video detection mode are set for each dangerous behavior monitoring range, so that one of the three behaviors is detected. For the dangerous behavior analysis in the dangerous behavior monitoring range in the passenger room, the CIF distinguishes the minimum detection size: 64 × 32 pixels; the minimum response time is less than 2 seconds, and the detection success rate is more than 80%.
Whether dangerous behaviors exist or not is only analyzed within a preset dangerous behavior monitoring range, whether dangerous behaviors exist or not is analyzed in a targeted mode, the possibility of misjudgment is reduced, the early warning accuracy rate of the dangerous behaviors is increased, and meanwhile the data volume analyzed by an analysis host is also reduced.
The monitoring server sends real-time videos of the acquisition devices in the railway train passenger room area to the analysis host in a multicast mode, the analysis host analyzes and processes the videos, and corresponding early warning is generated once dangerous behavior conditions occur.
The analysis host computer analyzes whether dangerous behaviors exist or not by acquiring a series of special static and dynamic characteristics of images of the video to realize the description and the judgment of specific events. In order to realize the analysis of dangerous behaviors, optical flow, cluster analysis, image feature description, classifier and other computer vision and pattern recognition technologies are used.
How the monitoring system achieves the early warning that the protection area is invaded is explained below.
Fig. 4 is a flowchart of an analysis host of the monitoring system shown in fig. 1 for analyzing whether a protection area is invaded. In implementation, the preset early warning condition further comprises that a preset target is in the protection area;
as shown in fig. 4, the analysis host is specifically configured to analyze whether the protection area is invaded, and includes step S400: identifying whether a preset target exists in a preset intrusion behavior monitoring range in the video of the protection area:
step S410: when the preset target exists, sending early warning information that a protection area is invaded to the early warning device;
step S420: if the preset target does not exist, not sending early warning information that a protection area is invaded to the early warning device;
the early warning device is specifically used for carrying out early warning that the protection area is invaded according to the early warning information that the protection area is invaded.
The method comprises the steps of setting a protection area aiming at areas which need special protection and do not allow non-workers to enter, such as a driver cab, a mechanic cab and the like of the rail train, and presetting an intrusion behavior monitoring range in a video of the protection area, such as the periphery of a doorway. And analyzing whether the protected area is invaded or not according to the video of the protected area, wherein the analysis is based on whether a person exists in the invasion behavior monitoring range in the protected area:
when the preset target is identified in the intrusion behavior monitoring range, sending early warning information of intrusion of a protection area to the early warning device;
when the preset target is not identified in the intrusion behavior monitoring range, not sending early warning information of the intrusion of the protection area;
the early warning device is specifically used for carrying out early warning that the protection area is invaded according to the early warning information that the protection area is invaded.
The device can be used for deploying defense for key areas (such as a driver cab, a mechanical engineer room and the like) through the acquisition device arranged in the carriage, and safety precaution and protection for the key areas of the rail train can be realized by setting a monitoring area, a monitoring area and early warning time and an early warning output mode.
And (4) early warning analysis on whether the protected area is invaded or not, and adopting a behavior analysis technology depending on a track. The basic method is that a background image is obtained by using a continuously input image sequence as a reference, a subsequently entered image is compared with the background image to obtain different pixel points, then connectivity marking is carried out on the pixel points, the marked areas are initial targets, then the targets are tracked to form a continuous tracking track, and then the foreground and the tracking track are analyzed; and comparing the information with preset rule information, and outputting early warning information.
The intrusion behavior monitoring range can be set to be an arbitrary polygonal intrusion behavior monitoring range;
(1) a plurality of independent intrusion behavior monitoring ranges can be set in the same scene;
(2) for each intrusion behavior monitoring range, one or two of boundary crossing the intrusion behavior monitoring range and two behavior detection in the intrusion behavior monitoring range can be set;
(3) for monitoring within the intrusion behavior monitoring range, the number of preset targets, the shortest alarm time, the repeated alarm interval time and the like can be set;
(4) for a preset target crossing the boundary of the intrusion behavior monitoring range, the crossing direction can be set to be entering, leaving or bidirectional, whether the protection area is monitored by intrusion is under CIF resolution, and the size of the minimum detection target is as follows: 10 x 10 pixels, response time less than 1 second, monitoring success rate greater than 90%.
How the monitoring system achieves the early warning of the person congestion will be described below.
Fig. 5 is a flowchart of the analysis of whether or not the person is crowded by the analysis host of the monitoring system shown in fig. 1. In implementation, the preset early warning condition further comprises that a personnel number threshold is reached or exceeded;
as shown in fig. 5, the analysis host is specifically configured to analyze whether or not the person is crowded, and includes step S500: whether the number of preset targets identified from the preset congestion monitoring range in the video of the designated area reaches or exceeds a personnel number threshold value:
step S510: when the number of people reaches or exceeds a threshold value of the number of people, sending early warning information of people crowding to the early warning device;
step S520: when the number of the people does not reach or exceed the threshold value of the number of the people, the early warning information of people crowding is not sent;
the early warning device is specifically used for carrying out early warning of the crowdedness according to the early warning information of the crowdedness.
Setting an acquisition device for a position where a rail train is easy to be crowded, namely the position where the rail train is easy to be crowded is a designated area, such as a passageway from an entrance of the rail train to an entrance of a passenger room; and in the preset congestion monitoring range in the video of the designated area, the positions where the human faces at the high positions of the aisle cannot reach are excluded in the height range where the human faces are usually located. And sending crowd early warning information to the early warning device when the number of the identified preset targets reaches or exceeds a staff number threshold value within a preset crowd monitoring range in the video of the designated area, thereby realizing early warning of the crowd.
How the monitoring system achieves the sound anomaly early warning is explained below.
In an implementation, the monitoring data further comprises audio; the preset early warning condition also comprises that a sound early warning threshold value is reached or exceeded;
the analysis host is further used for analyzing whether the sound is abnormal or not, and comprises the following steps: if the sound level of the audio reaches or exceeds a sound early warning threshold:
when the sound early warning threshold value is reached or exceeded, sending early warning information of abnormal sound to the early warning device;
if the sound does not reach or exceed the sound early warning threshold value, the early warning information of abnormal sound is not sent to the early warning device;
the early warning device is also used for carrying out sound abnormity early warning according to the early warning information of the sound abnormity.
The acquisition devices on two sides of the carriage of the rail train are cameras with sound pick-up devices, and can acquire audio in real time and analyze and process the audio by the analysis host. And if the sound intensity exceeds the sound early warning threshold value, generating sound abnormity early warning. The intensity of the sound of the audio can be set; the shortest duration of the sound alarm can be set, the minimum response time to the sound abnormity is 1 second, and the detection success rate is more than 90%.
How the monitoring system achieves early warning of key deployment and control personnel is explained below.
In implementation, the monitoring system further comprises a face database;
the analysis host is also used for analyzing whether key deployment and control personnel are found, and comprises the following steps: capturing a face image from the video, comparing and identifying the captured face image with a face in the face database, and sending early warning information for finding key deployment and control personnel to the early warning device when the captured face image is matched with the face in the face database;
the early warning device is also used for carrying out early warning for discovering key deployment and control personnel according to the early warning information of the key deployment and control personnel.
Therefore, the early warning of key deployment and control personnel is realized.
The early warning of key control personnel needs to collect the information of the passengers, and the retrieval and early warning of the key control personnel are realized through the comparison of the analysis host. The specific implementation is that the acquisition device realizes video snapshot, and the analysis host realizes the feature import and retrieval comparison query of the database.
The analysis host is mainly divided into an image capturing module, an image comparison module, a human face database management module and a human face feature retrieval module:
(1) an image capture module: capturing real-time images in a high-speed rail carriage through an acquisition device in the rail train, detecting face images in captured video images every a plurality of frames, and transmitting the face images to an image comparison module for comparison;
(2) an image comparison module: extracting the human face features in the image capturing module;
(3) the human face database management module: managing feature data of a face already recorded in the system;
(4) the face feature retrieval module: and searching whether the extracted face features exist in a face database and judging whether the extracted face features are the faces of the key deployment and control personnel.
The core of the early warning of the important deployment and control personnel is face recognition, and a face recognition algorithm comprises three parts: face detection, face key detection and face recognition. The face detection is to find all faces contained in one picture, the face key point detection is to detect the key point coordinates of the faces on the detected face image so as to estimate the pose of the faces, the face identification is to change the faces into vectors with specific dimensions, and whether the face images are the face images of the same person is judged according to the similarity of the vectors.
In implementation, the analysis host performs at least one analysis on the monitoring data acquired by each acquisition device according to the setting position of the acquisition device, including an analysis on whether dangerous behaviors exist, an analysis on whether a protected area is invaded, an analysis on whether people are crowded, an analysis on whether sounds are abnormal, and an analysis on whether important deployment and control personnel are found.
Therefore, the monitoring data acquired by the acquisition device are respectively analyzed according to the position of the acquisition device, and the monitoring data are fully utilized.
Specifically, the protection area comprises a driver cab and/or a mechanic's cab of the rail train.
Specifically, the acquisition device comprises a panoramic camera and a hemispherical camera with a sound pickup function;
as shown in fig. 2, the panoramic camera 120 is disposed at a passing station of the rail train;
four hemispherical cameras 110 are arranged in the rail train.
In real time, the monitoring system further comprises a monitoring screen, and the monitoring screen is arranged in each of the mechanic rooms;
the monitoring screen is used for displaying a real-time monitoring picture and is used for displaying the early warning information.
Example two
The rail train monitoring method comprises the following steps:
acquiring monitoring data in the rail train, wherein the monitoring data comprises videos;
receiving and storing the monitoring data, and transmitting the monitoring data;
identifying and analyzing a preset target from the monitoring data, and sending early warning information when the behavior of the preset target meets a preset early warning condition;
and receiving early warning information and carrying out early warning.
In implementation, identifying and analyzing a preset target from the monitoring data, and when the behavior of the preset target meets a preset early warning condition, the step of sending early warning information specifically includes:
analyzing whether dangerous behaviors exist or not, wherein the analysis comprises the steps of identifying and analyzing the preset target from a dangerous behavior monitoring range preset in a video of a passenger room area of the rail train, and judging whether the behavior action amplitude of the preset target reaches or exceeds an action amplitude threshold value or not in the dangerous behavior monitoring range:
when the action amplitude threshold value is reached or exceeded, sending early warning information of dangerous behaviors; wherein the early warning condition comprises reaching or exceeding an action amplitude threshold value;
receiving early warning information, and the early warning step specifically comprises the following steps:
and carrying out early warning on the dangerous behaviors according to the early warning information of the dangerous behaviors.
In implementation, identifying and analyzing a preset target from the monitoring data, and when the behavior of the preset target reaches a preset early warning condition, the step of sending early warning information specifically includes:
analyzing whether a protection area is invaded or not, wherein the analysis comprises the steps of identifying whether the preset target exists or not from a preset invasion behavior monitoring range in a video of the protection area:
when the preset target exists, sending early warning information that the protection area is invaded; the early warning condition also comprises that a preset target is in the protection area;
receiving early warning information, and the early warning step specifically comprises the following steps:
and according to the early warning information of the intrusion of the protection area, early warning of the intrusion of the protection area is carried out.
In implementation, identifying and analyzing a preset target from the monitoring data, and when the behavior of the preset target reaches a preset early warning condition, the step of sending early warning information specifically includes:
analyzing whether the personnel are crowded, wherein the analysis comprises the steps of identifying whether the number of preset targets in a preset crowded monitoring range in a video of a designated area reaches or exceeds a personnel number threshold value:
when the number of the people reaches or exceeds the threshold value of the number of the people, sending early warning information of people crowding; the preset early warning condition also comprises that the number of people reaches or exceeds a personnel number threshold value;
receiving early warning information, and the early warning step specifically comprises the following steps:
and carrying out the early warning of the crowding of the personnel according to the early warning information of the crowding of the personnel.
In implementation, the rail train monitoring method further includes:
and analyzing whether the sound is abnormal or not, wherein the analysis comprises the following steps that whether the sound size of the audio reaches or exceeds a sound early warning threshold value or not:
when the sound early warning threshold value is reached or exceeded, sending early warning information of abnormal sound; the monitoring data further comprises audio, and the preset early warning condition further comprises that a sound early warning threshold value is reached or exceeded;
and carrying out sound abnormity early warning according to the early warning information of the sound abnormity.
In implementation, the rail train monitoring method further includes:
and (3) carrying out analysis on whether key deployment personnel are found, wherein the analysis comprises the following steps: capturing a face image from the video, comparing and identifying the captured face image with a face in a face database, and sending early warning information for finding key deployment and control personnel when the captured face image is matched with the face in the face database;
and early warning for discovering key deployment and control personnel is carried out according to the early warning information for discovering key deployment and control personnel.
EXAMPLE III
The monitoring device of rail train of this application embodiment includes:
the acquisition module is used for acquiring monitoring data in the rail train, wherein the monitoring data comprises videos;
the receiving and storing module is used for receiving and storing the monitoring data and transmitting the monitoring data;
the analysis module is used for identifying and analyzing a preset target from the monitoring data, and sending early warning information when the behavior of the preset target meets a preset early warning condition;
and the early warning module is used for receiving the early warning information and carrying out early warning.
In an implementation, the analysis module includes:
and the dangerous behavior analysis submodule is used for analyzing whether dangerous behaviors exist or not, and comprises the steps of identifying and analyzing the preset target from a dangerous behavior monitoring range preset in a video of the railway train passenger room area, and judging whether the behavior action amplitude of the preset target reaches or exceeds an action amplitude threshold value or not in the dangerous behavior monitoring range:
when the action amplitude threshold value is reached or exceeded, sending early warning information of dangerous behaviors; wherein the early warning condition comprises reaching or exceeding an action amplitude threshold value;
the early warning module includes:
and the dangerous behavior early warning submodule is used for carrying out early warning on dangerous behaviors according to the early warning information of the dangerous behaviors.
In an implementation, the analysis module further comprises:
the invaded analysis submodule is used for analyzing whether the protection area is invaded or not, and comprises: identifying whether the preset target exists in a preset intrusion behavior monitoring range in the video of the protection area:
when the preset target exists, sending early warning information that the protection area is invaded; the preset early warning condition also comprises that a preset target is in the protection area;
the early warning module further comprises:
and the invaded early warning submodule is used for carrying out early warning on the invasion of the protection area according to the invaded early warning information of the protection area.
In an implementation, the analysis module further comprises:
the congestion analysis submodule is used for analyzing whether people are congested, and comprises: whether the number of preset targets identified from the preset congestion monitoring range in the video of the designated area reaches or exceeds a personnel number threshold value:
when the number of the people reaches or exceeds the threshold value of the number of the people, sending early warning information of people crowding; the preset early warning condition also comprises that the number of people reaches or exceeds a personnel number threshold value;
the early warning module further comprises:
and the congestion early warning submodule is used for early warning the congestion of the personnel according to the early warning information of the congestion of the personnel.
In an implementation, the analysis module further comprises:
the sound analysis submodule is used for analyzing whether the sound is abnormal or not, and comprises the following steps that whether the sound size of the audio reaches or exceeds a sound early warning threshold value or not:
when the sound early warning threshold value is reached or exceeded, sending early warning information of abnormal sound; the monitoring data further comprises audio, and the preset early warning condition further comprises that a sound early warning threshold value is reached or exceeded;
the early warning module further comprises:
and the sound abnormity early warning submodule is used for carrying out sound abnormity early warning according to the early warning information of the sound abnormity.
In an implementation, the analysis module further comprises:
the key deployment and control personnel analysis submodule is used for analyzing whether key deployment and control personnel are found or not, and comprises: capturing a face image from the video, comparing and identifying the captured face image with a face in a face database, and sending early warning information for finding key deployment and control personnel when the captured face image is matched with the face in the face database;
the early warning module further comprises:
and the early warning submodule for discovering key deployment and control personnel is used for early warning for discovering key deployment and control personnel according to the early warning information of the key deployment and control personnel.
In the description of the present application and the embodiments thereof, it is to be understood that the terms "top", "bottom", "height", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present application.
In this application and its embodiments, unless expressly stated or limited otherwise, the terms "disposed," "mounted," "connected," "secured," and the like are to be construed broadly, e.g., as meaning fixedly connected, detachably connected, or integral to; the connection can be mechanical connection, electrical connection or communication; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In this application and its embodiments, unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may comprise the first and second features being in direct contact, or may comprise the first and second features being in contact, not directly, but via another feature in between. Also, the first feature being "on," "above" and "over" the second feature includes the first feature being directly on and obliquely above the second feature, or merely indicating that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature includes the first feature being directly above and obliquely above the second feature, or simply meaning that the first feature is at a lesser level than the second feature.
The above disclosure provides many different embodiments or examples for implementing different structures of the application. The components and arrangements of specific examples are described above to simplify the present disclosure. Of course, they are merely examples and are not intended to limit the present application. Moreover, the present application may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, examples of various specific processes and materials are provided herein, but one of ordinary skill in the art may recognize applications of other processes and/or use of other materials.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (22)
1. A monitoring system for a rail train, comprising:
the acquisition device is used for acquiring monitoring data in the rail train, and the monitoring data comprises videos;
the monitoring server is connected with the acquisition device to receive and store the monitoring data and transmit the monitoring data to each analysis host;
the analysis hosts are respectively arranged in the carriages of the rail train and used for identifying and analyzing preset targets from the monitoring data and sending early warning information to an early warning device when the behavior of the preset targets meets preset early warning conditions;
and the early warning device is connected with the monitoring server and is used for carrying out early warning after receiving the early warning information forwarded by the monitoring server.
2. The rail train monitoring system of claim 1, wherein the pre-warning condition includes reaching or exceeding an action amplitude threshold;
the analysis host is specifically configured to analyze whether dangerous behaviors exist, and includes identifying and analyzing the preset target from a dangerous behavior monitoring range preset in a video of the rail train passenger room area, and determining whether a behavior action amplitude of the preset target in the dangerous behavior monitoring range reaches or exceeds an action amplitude threshold value:
when the action amplitude threshold value is reached or exceeded, sending early warning information of dangerous behaviors to the early warning device;
the early warning device is specifically used for carrying out early warning on dangerous behaviors according to the early warning information of the dangerous behaviors.
3. The rail train monitoring system of claim 2, wherein the pre-warning condition further includes a preset target in a preset intrusion behavior monitoring range in the protected area;
the analysis host is specifically configured to analyze whether a protection area is invaded, including identifying whether the preset target exists from an invasion behavior monitoring range preset in a video of the protection area:
when the preset target exists, sending early warning information that a protection area is invaded to the early warning device;
the early warning device is specifically used for carrying out early warning that the protection area is invaded according to the early warning information that the protection area is invaded.
4. The rail train monitoring system of claim 3, wherein the pre-warning condition further comprises reaching or exceeding a number of people threshold;
the analysis host is specifically configured to analyze whether people are crowded, including whether the number of preset targets identified from a preset crowd monitoring range in a video of a specified area reaches or exceeds a people number threshold:
when the number of people reaches or exceeds a threshold value of the number of people, sending early warning information of people crowding to the early warning device;
the early warning device is specifically used for carrying out early warning of the crowdedness according to the early warning information of the crowdedness.
5. The rail train monitoring system of claim 4, wherein the monitoring data further comprises audio; the early warning condition also comprises that a sound early warning threshold value is reached or exceeded;
the analysis host is further used for analyzing whether the sound is abnormal or not, and comprises the following steps: if the sound level of the audio reaches or exceeds a sound early warning threshold:
when the sound early warning threshold value is reached or exceeded, sending early warning information of abnormal sound to the early warning device;
the early warning device is also used for carrying out sound abnormity early warning according to the early warning information of the sound abnormity.
6. The rail train monitoring system of claim 5, further comprising a face database;
the analysis host is also used for analyzing whether key deployment and control personnel are found, and comprises the following steps: capturing a face image from the video, comparing and identifying the captured face image with a face in the face database, and sending early warning information for finding key deployment and control personnel to the early warning device when the captured face image is matched with the face in the face database;
the early warning device is also used for carrying out early warning for discovering key deployment and control personnel according to the early warning information of the key deployment and control personnel.
7. The monitoring system for a rail train according to claim 6, wherein the analysis host performs at least one analysis of the monitoring data acquired by each of the acquisition devices according to the installation location of the acquisition device, including an analysis of whether dangerous behavior exists, an analysis of whether a protected area is invaded, an analysis of whether a person is crowded, an analysis of whether a sound is abnormal, and an analysis of whether a key deployment and control person is found.
8. The rail train monitoring system of claim 7, wherein the protected area comprises a cab and/or a mechanic's room of the rail train.
9. The monitoring system of the rail train according to claim 8, wherein the acquiring means includes a panoramic camera and a hemispherical camera having a sound pickup function;
the panoramic camera is arranged at a passing platform of the rail train;
four hemispherical cameras are arranged in the rail train.
10. The rail train monitoring system of claim 9, wherein the early warning device includes a monitoring screen disposed within a chamber of each of the mechanic's chambers;
the monitoring screen is used for displaying a real-time monitoring picture and is used for displaying the early warning information.
11. A rail train monitoring method is characterized by comprising the following steps:
acquiring monitoring data in the rail train, wherein the monitoring data comprises videos;
receiving and storing the monitoring data, and transmitting the monitoring data;
identifying and analyzing a preset target from the monitoring data, and sending early warning information when the behavior of the preset target meets a preset early warning condition;
and receiving early warning information and carrying out early warning.
12. The rail train monitoring method according to claim 11, wherein a preset target is identified and analyzed from the monitoring data, and when a behavior of the preset target meets a preset early warning condition, the step of sending early warning information specifically includes:
analyzing whether dangerous behaviors exist or not, wherein the analysis comprises the steps of identifying and analyzing the preset target from a dangerous behavior monitoring range preset in a video of a passenger room area of the rail train, and judging whether the behavior action amplitude of the preset target reaches or exceeds an action amplitude threshold value or not in the dangerous behavior monitoring range:
when the action amplitude threshold value is reached or exceeded, sending early warning information of dangerous behaviors; wherein the early warning condition comprises reaching or exceeding an action amplitude threshold value;
receiving early warning information, and the early warning step specifically comprises the following steps:
and carrying out early warning on the dangerous behaviors according to the early warning information of the dangerous behaviors.
13. The rail train monitoring method according to claim 12, wherein a preset target is identified and analyzed from the monitoring data, and when a behavior of the preset target reaches a preset early warning condition, the step of sending early warning information specifically includes:
analyzing whether a protection area is invaded, wherein the analysis comprises the steps that whether the preset target exists or not is identified in a preset invasion behavior monitoring range in a video of the protection area:
when the preset target exists, sending early warning information that the protection area is invaded; the early warning condition also comprises that a preset target is in the protection area;
receiving early warning information, and the early warning step specifically comprises the following steps:
and according to the early warning information of the intrusion of the protection area, early warning of the intrusion of the protection area is carried out.
14. The rail train monitoring method according to claim 13, wherein a preset target is identified and analyzed from the monitoring data, and when a behavior of the preset target reaches a preset early warning condition, the step of sending early warning information specifically includes:
analyzing whether the personnel are crowded, wherein the analysis comprises the steps of identifying whether the number of preset targets in a preset crowded monitoring range in a video of a designated area reaches or exceeds a personnel number threshold value:
when the number of the people reaches or exceeds the threshold value of the number of the people, sending early warning information of people crowding; the preset early warning condition also comprises that the number of people reaches or exceeds a personnel number threshold value;
receiving early warning information, and the early warning step specifically comprises the following steps:
and carrying out the early warning of the crowding of the personnel according to the early warning information of the crowding of the personnel.
15. The rail train monitoring method according to claim 14, further comprising:
and analyzing whether the sound is abnormal or not, wherein the analysis comprises the following steps that whether the sound size of the audio reaches or exceeds a sound early warning threshold value or not:
when the sound early warning threshold value is reached or exceeded, sending early warning information of abnormal sound; the monitoring data further comprises audio, and the preset early warning condition further comprises that a sound early warning threshold value is reached or exceeded;
and carrying out sound abnormity early warning according to the early warning information of the sound abnormity.
16. The rail train monitoring method according to claim 15, further comprising:
and (3) carrying out analysis on whether key deployment personnel are found, wherein the analysis comprises the following steps: capturing a face image from the video, comparing and identifying the captured face image with a face in a face database, and sending early warning information for finding key deployment and control personnel when the captured face image is matched with the face in the face database;
and early warning for discovering key deployment and control personnel is carried out according to the early warning information for discovering key deployment and control personnel.
17. A monitoring device for a rail train, comprising:
the acquisition module is used for acquiring monitoring data in the rail train, wherein the monitoring data comprises videos;
the receiving and storing module is used for receiving and storing the monitoring data and transmitting the monitoring data;
the analysis module is used for identifying and analyzing a preset target from the monitoring data, and sending early warning information when the behavior of the preset target meets a preset early warning condition;
and the early warning module is used for receiving the early warning information and carrying out early warning.
18. The rail train monitoring device of claim 17, wherein the analysis module comprises:
and the dangerous behavior analysis submodule is used for analyzing whether dangerous behaviors exist or not, and comprises the steps of identifying and analyzing the preset target from a dangerous behavior monitoring range preset in a video of the railway train passenger room area, and judging whether the behavior action amplitude of the preset target reaches or exceeds an action amplitude threshold value or not in the dangerous behavior monitoring range:
when the action amplitude threshold value is reached or exceeded, sending early warning information of dangerous behaviors; wherein the early warning condition comprises reaching or exceeding an action amplitude threshold value;
the early warning module includes:
and the dangerous behavior early warning submodule is used for carrying out early warning on dangerous behaviors according to the early warning information of the dangerous behaviors.
19. The rail train monitoring device of claim 18, wherein the analysis module further comprises:
the invaded analysis submodule is used for analyzing whether the protection area is invaded or not, and comprises: identifying whether the preset target exists in a preset intrusion behavior monitoring range in the video of the protection area:
when the preset target exists, sending early warning information that the protection area is invaded; the preset early warning condition also comprises that a preset target is in the protection area;
the early warning module further comprises:
and the invaded early warning submodule is used for carrying out early warning on the invasion of the protection area according to the invaded early warning information of the protection area.
20. The rail train monitoring device of claim 19, wherein the analysis module further comprises:
the congestion analysis submodule is used for analyzing whether people are congested, and comprises: whether the number of preset targets identified from the preset congestion monitoring range in the video of the designated area reaches or exceeds a personnel number threshold value:
when the number of the people reaches or exceeds the threshold value of the number of the people, sending early warning information of people crowding; the preset early warning condition also comprises that the number of people reaches or exceeds a personnel number threshold value;
the early warning module further comprises:
and the congestion early warning submodule is used for early warning the congestion of the personnel according to the early warning information of the congestion of the personnel.
21. The rail train monitoring device of claim 20, wherein the analysis module further comprises:
the sound analysis submodule is used for analyzing whether the sound is abnormal or not, and comprises: if the sound level of the audio reaches or exceeds a sound early warning threshold:
when the sound early warning threshold value is reached or exceeded, sending early warning information of abnormal sound; the monitoring data further comprises audio, and the preset early warning condition further comprises that a sound early warning threshold value is reached or exceeded;
the early warning module further comprises:
and the sound abnormity early warning submodule is used for carrying out sound abnormity early warning according to the early warning information of the sound abnormity.
22. The rail train monitoring device of claim 21, wherein the analysis module further comprises:
the key deployment and control personnel analysis submodule is used for analyzing whether key deployment and control personnel are found or not, and comprises: capturing a face image from the video, comparing and identifying the captured face image with a face in a face database, and sending early warning information for finding key deployment and control personnel when the captured face image is matched with the face in the face database;
the early warning module further comprises:
and the early warning submodule for discovering key deployment and control personnel is used for early warning for discovering key deployment and control personnel according to the early warning information of the key deployment and control personnel.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010089249.XA CN113256924A (en) | 2020-02-12 | 2020-02-12 | Monitoring system, monitoring method and monitoring device for rail train |
| PCT/CN2020/085920 WO2021159604A1 (en) | 2020-02-12 | 2020-04-21 | Monitoring system, monitoring method, and monitoring device for railway train |
| EP20919029.7A EP4105101A4 (en) | 2020-02-12 | 2020-04-21 | Monitoring system, monitoring method, and monitoring device for railway train |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010089249.XA CN113256924A (en) | 2020-02-12 | 2020-02-12 | Monitoring system, monitoring method and monitoring device for rail train |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113256924A true CN113256924A (en) | 2021-08-13 |
Family
ID=77219738
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010089249.XA Pending CN113256924A (en) | 2020-02-12 | 2020-02-12 | Monitoring system, monitoring method and monitoring device for rail train |
Country Status (3)
| Country | Link |
|---|---|
| EP (1) | EP4105101A4 (en) |
| CN (1) | CN113256924A (en) |
| WO (1) | WO2021159604A1 (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114148378A (en) * | 2021-12-31 | 2022-03-08 | 安徽徽一通讯科技有限公司 | Audible and visual alarm for railway section operation protection |
| CN114228794A (en) * | 2021-12-17 | 2022-03-25 | 神思电子技术股份有限公司 | Automatic monitoring method and equipment for CTC scheduling |
| CN114722102A (en) * | 2022-04-24 | 2022-07-08 | 武汉北曦盛科技有限公司 | Intelligent monitoring and management system for rail transit energy consumption system based on big data analysis |
| CN114715220A (en) * | 2022-06-07 | 2022-07-08 | 深圳龙铁高科技术有限公司 | Intelligent early warning method and early warning system for locomotive |
| CN116189305A (en) * | 2023-03-09 | 2023-05-30 | 合肥市轨道交通集团有限公司 | Personnel dangerous action recognition method based on neural network model embedding |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114066157B (en) * | 2021-10-15 | 2024-06-28 | 江苏禹治流域管理技术研究院有限公司 | Sand production data early warning analysis system |
| CN113850967B (en) * | 2021-10-18 | 2022-05-27 | 江苏立源自动化工程有限公司 | Railway wagon operation safety and fault early warning system based on coal loading system |
| CN114173220B (en) * | 2021-12-03 | 2023-06-27 | 中国铁道科学研究院集团有限公司标准计量研究所 | Railway cargo transportation monitoring data transmission method and device |
| CN114863349A (en) * | 2022-06-30 | 2022-08-05 | 中关村科学城城市大脑股份有限公司 | Foundation pit safety protection monitoring method and system |
| CN115009324B (en) * | 2022-07-08 | 2023-11-03 | 成都西交轨道交通技术服务有限公司 | Rail transit unstable network accident early warning system and method based on cloud edge cooperation |
| CN115378968B (en) * | 2022-07-20 | 2024-06-04 | 慧之安信息技术股份有限公司 | Biological laboratory safety detection system based on edge calculation |
| CN116363817B (en) * | 2023-02-02 | 2024-01-02 | 淮阴工学院 | Chemical plant dangerous area invasion early warning method and system |
| CN116743970B (en) * | 2023-08-14 | 2023-11-21 | 安徽塔联智能科技有限责任公司 | Intelligent management platform with video AI early warning analysis |
| CN117251440B (en) * | 2023-09-20 | 2024-05-07 | 华安钢宝利高新汽车板加工(娄底)有限公司 | Automatic correction system and method for data transmission for development of driving computer |
| CN117395685B (en) * | 2023-10-30 | 2024-07-09 | 中国铁建电气化局集团有限公司 | High-speed railway wireless network optimizing system based on artificial intelligence |
| CN117557966B (en) * | 2024-01-09 | 2024-04-02 | 南京格瑞物联科技有限公司 | Campus abnormal behavior safety detection method and system based on monitoring image recognition |
| CN118155357A (en) * | 2024-03-07 | 2024-06-07 | 广州市烨兴融集团有限公司 | Intelligent office video monitoring management method, system, equipment and medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101174298A (en) * | 2006-11-02 | 2008-05-07 | 上海银晨智能识别科技有限公司 | Scattered-point high-volume face recognition system and recognizing method thereof |
| CN101917604A (en) * | 2010-09-03 | 2010-12-15 | 中铁上海设计院集团有限公司 | Intelligent analysis method for video images in rail transit |
| US20110316697A1 (en) * | 2010-06-29 | 2011-12-29 | General Electric Company | System and method for monitoring an entity within an area |
| CN202818505U (en) * | 2012-09-25 | 2013-03-20 | 毛振刚 | Train compartment video monitoring and managing system |
| CN103297751A (en) * | 2013-04-23 | 2013-09-11 | 四川天翼网络服务有限公司 | Wisdom skynet video behavior analyzing system |
| CN103973653A (en) * | 2013-02-01 | 2014-08-06 | 上海迪爱斯通信设备有限公司 | Intelligent sensing analyzer |
| CN206100297U (en) * | 2016-08-16 | 2017-04-12 | 山西省交通科学研究院 | On -vehicle monitored control system of train |
| CN109606427A (en) * | 2019-01-11 | 2019-04-12 | 东北石油大学 | Compartment safety warning method based on ZigBee |
| CN110525456A (en) * | 2019-08-07 | 2019-12-03 | 北京全路通信信号研究设计院集团有限公司 | Train safe driving monitoring system and method |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100391184C (en) * | 2005-06-07 | 2008-05-28 | 沈阳泽通数码有限公司 | Digital safeguard video monitoring system for railway train |
| DE102008008478A1 (en) * | 2008-02-08 | 2009-08-13 | Siemens Aktiengesellschaft | Video surveillance system for a vehicle and method for monitoring a vehicle |
| CN101795395B (en) * | 2009-02-04 | 2012-07-11 | 深圳市先进智能技术研究所 | System and method for monitoring crowd situation |
| CN102254401A (en) * | 2011-05-11 | 2011-11-23 | 北京城市***工程研究中心 | Intelligent analyzing method for passenger flow motion |
| CN102385791A (en) * | 2011-11-08 | 2012-03-21 | 天津银箭科技有限公司 | Electronic map video and alarm monitoring linked system |
| WO2014109039A1 (en) * | 2013-01-10 | 2014-07-17 | 三菱電機株式会社 | Vehicle interior monitoring system, and vehicle interior monitoring method |
| US10392038B2 (en) * | 2016-05-16 | 2019-08-27 | Wi-Tronix, Llc | Video content analysis system and method for transportation system |
| CN205847433U (en) * | 2016-07-05 | 2016-12-28 | 北京清网华科技有限公司 | A kind of Vehicular video monitoring system |
| GB2556942A (en) * | 2016-11-28 | 2018-06-13 | Univ Of Lancaster | Transport passenger monitoring systems |
| CN108297899A (en) * | 2018-02-07 | 2018-07-20 | 北京明铁科技有限公司 | A kind of train safety monitoring early warning system |
| CN108900814A (en) * | 2018-07-27 | 2018-11-27 | 安徽安为科技有限公司 | A kind of train Active Eyes |
| CN109543538A (en) * | 2018-10-23 | 2019-03-29 | 深圳壹账通智能科技有限公司 | Obtain method, apparatus, computer equipment and the storage medium of the track of alert object |
| CN110544360B (en) * | 2019-08-07 | 2022-04-08 | 北京全路通信信号研究设计院集团有限公司 | Train safe driving monitoring system and method |
| CN110588729A (en) * | 2019-10-22 | 2019-12-20 | 西南交通大学 | Railway monitoring auxiliary system based on big data |
-
2020
- 2020-02-12 CN CN202010089249.XA patent/CN113256924A/en active Pending
- 2020-04-21 EP EP20919029.7A patent/EP4105101A4/en active Pending
- 2020-04-21 WO PCT/CN2020/085920 patent/WO2021159604A1/en unknown
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101174298A (en) * | 2006-11-02 | 2008-05-07 | 上海银晨智能识别科技有限公司 | Scattered-point high-volume face recognition system and recognizing method thereof |
| US20110316697A1 (en) * | 2010-06-29 | 2011-12-29 | General Electric Company | System and method for monitoring an entity within an area |
| CN101917604A (en) * | 2010-09-03 | 2010-12-15 | 中铁上海设计院集团有限公司 | Intelligent analysis method for video images in rail transit |
| CN202818505U (en) * | 2012-09-25 | 2013-03-20 | 毛振刚 | Train compartment video monitoring and managing system |
| CN103973653A (en) * | 2013-02-01 | 2014-08-06 | 上海迪爱斯通信设备有限公司 | Intelligent sensing analyzer |
| CN103297751A (en) * | 2013-04-23 | 2013-09-11 | 四川天翼网络服务有限公司 | Wisdom skynet video behavior analyzing system |
| CN206100297U (en) * | 2016-08-16 | 2017-04-12 | 山西省交通科学研究院 | On -vehicle monitored control system of train |
| CN109606427A (en) * | 2019-01-11 | 2019-04-12 | 东北石油大学 | Compartment safety warning method based on ZigBee |
| CN110525456A (en) * | 2019-08-07 | 2019-12-03 | 北京全路通信信号研究设计院集团有限公司 | Train safe driving monitoring system and method |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114228794A (en) * | 2021-12-17 | 2022-03-25 | 神思电子技术股份有限公司 | Automatic monitoring method and equipment for CTC scheduling |
| CN114228794B (en) * | 2021-12-17 | 2023-09-22 | 神思电子技术股份有限公司 | Automatic monitoring method and equipment for CTC scheduling |
| CN114148378A (en) * | 2021-12-31 | 2022-03-08 | 安徽徽一通讯科技有限公司 | Audible and visual alarm for railway section operation protection |
| CN114148378B (en) * | 2021-12-31 | 2022-07-19 | 安徽徽一通讯科技有限公司 | Audible and visual alarm for railway section operation protection |
| CN114722102A (en) * | 2022-04-24 | 2022-07-08 | 武汉北曦盛科技有限公司 | Intelligent monitoring and management system for rail transit energy consumption system based on big data analysis |
| CN114715220A (en) * | 2022-06-07 | 2022-07-08 | 深圳龙铁高科技术有限公司 | Intelligent early warning method and early warning system for locomotive |
| CN116189305A (en) * | 2023-03-09 | 2023-05-30 | 合肥市轨道交通集团有限公司 | Personnel dangerous action recognition method based on neural network model embedding |
| CN116189305B (en) * | 2023-03-09 | 2023-07-18 | 合肥市轨道交通集团有限公司 | Personnel dangerous action recognition method based on neural network model embedding |
Also Published As
| Publication number | Publication date |
|---|---|
| EP4105101A1 (en) | 2022-12-21 |
| WO2021159604A1 (en) | 2021-08-19 |
| EP4105101A4 (en) | 2023-08-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113256924A (en) | Monitoring system, monitoring method and monitoring device for rail train | |
| CN108802758B (en) | Intelligent security monitoring device, method and system based on laser radar | |
| KR101496390B1 (en) | System for Vehicle Number Detection | |
| KR102172239B1 (en) | Method and system for abnormal situation monitoring based on video | |
| KR101036947B1 (en) | The automatic guard system to prevent the crime and accident using computer video image analysis technology | |
| CN113676702B (en) | Video stream-based target tracking and monitoring method, system, device and storage medium | |
| KR101880100B1 (en) | CCTV System for detecting Object | |
| KR20090118582A (en) | Unexpcted accident monitoring system | |
| KR101492473B1 (en) | Context-aware cctv intergrated managment system with user-based | |
| KR101626377B1 (en) | A system for detecting car being violated parking and stopping of based on big date using CCTV camera and black box vehicle | |
| CN106529401A (en) | Vehicle anti-tracking method, vehicle anti-tracking device and vehicle anti-tracking system | |
| CN111046797A (en) | Oil pipeline warning method based on personnel and vehicle behavior analysis | |
| CN114359976B (en) | Intelligent security method and device based on person identification | |
| KR102434154B1 (en) | Method for tracking multi target in traffic image-monitoring-system | |
| KR102107957B1 (en) | Cctv monitoring system for detecting the invasion in the exterior wall of building and method thereof | |
| KR101485512B1 (en) | The sequence processing method of images through hippocampal neual network learning of behavior patterns in case of future crimes | |
| KR20210043960A (en) | Behavior Recognition Based Safety Monitoring System and Method using Artificial Intelligence Technology and IoT | |
| KR101814040B1 (en) | An integrated surveillance device using 3D depth information focus control | |
| CN111241918B (en) | Vehicle tracking prevention method and system based on face recognition | |
| KR101407952B1 (en) | Elevator crime prvent system and method of controlling the same | |
| KR101686851B1 (en) | Integrated control system using cctv camera | |
| CN108122414A (en) | The detection method and device of car on-board and off-board on highway | |
| KR102407202B1 (en) | Apparatus and method for intelligently analyzing video | |
| KR20130047131A (en) | Method and system for surveilling contents of surveillance using mobile terminal | |
| KR20200010691A (en) | Character recognition and traffic control system using heterogeneous camera |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210813 |
|
| RJ01 | Rejection of invention patent application after publication |