CN110473402B - Abnormal event detection and early warning system based on target abnormal behavior trajectory analysis - Google Patents
Abnormal event detection and early warning system based on target abnormal behavior trajectory analysisDownload PDFInfo
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Abstract
Description
Technical Field
The embodiment of the invention relates to the field of traffic monitoring, in particular to an abnormal event detection and early warning system based on target abnormal behavior track analysis.
Background
The existing road abnormal event detection equipment or system has the defects that the detection precision is insufficient, and the alarm cannot be detected in part of event types, such as collapse on a road, landslide, obstacles on the road, undetectable dangerous events and the like. At present, a plurality of event detection technologies exist in the traffic field and are used for detecting whether abnormal events or dangerous conditions exist in vehicles or areas in roads, bridges and tunnels. Such as a video event detection system based on video pattern analysis or video structuring, a radar event detection system based on millimeter wave radar target tracking technology, a laser radar event detection system based on laser radar scanning technology, a vehicle active warning system (vehicle SOS) based on vehicle GPS/beidou positioning, and the like.
The existing traffic incident detection system and equipment mostly adopt a video graphic analysis mode to detect, and the image provided by a camera installed on a road is used for analyzing whether abnormal vehicles or abnormal road conditions exist on the road by using an image processing technology and giving corresponding alarm information. The video event detection utilizes image analysis processing and a video structuring mode to analyze whether abnormal accident events exist in a picture, and the technology has stronger dependence on video images and has higher requirements on the installation position, the height and the surrounding environment of a camera outputting images in front, and the image comparison technology has poorer detection effect on special conditions, such as objects thrown or dropped, particularly objects without motion tracks after dropping, and can not accurately detect the events and send out an alarm. Especially, under the environment (smoke and dust) with weak illumination or at night and under the condition of severe meteorological conditions (fog, snow, rain and haze), the detection precision of the video event is greatly reduced, and even the event can not be detected and alarmed. The risk of occurrence of subsequent traffic accidents is greatly increased.
The millimeter wave radar event detection system is based on the fact that dynamic information of a target is obtained by a radar through transmitting radio waves and passing echoes, most of existing radar equipment adopts Doppler technology, the technology is interested in moving high-speed targets, and slow objects below 5Km/h and stopped objects cannot be tracked and detected, for example: stopped vehicles, pedestrians, objects spilled, road surface collapses, landslides, and the like.
The abnormal event detection based on the vehicle GPS/Beidou positioning system is mainly completed by the positioning system and the rescue calling system which are installed on the vehicle, when the vehicle is abnormal or a driver and a passenger send a call for help, the vehicle monitoring center can obtain the alarm information of the accident vehicle, but cannot obtain the road condition information and other accident early-warning information which is not the vehicle.
Disclosure of Invention
Therefore, the embodiment of the invention provides an abnormal event detection and early warning system based on target abnormal behavior trajectory analysis, which is used for solving the problems that the conventional abnormal event detection system on the road is low in detection precision, part of event types cannot be detected and early warning cannot be realized.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
the embodiment of the invention discloses an abnormal event detection and early warning system based on target abnormal behavior track analysis, which comprises: the system comprises a target behavior track acquisition device, a pan-tilt camera and a server, wherein the target behavior track acquisition device acquires a behavior track of a target and then transmits real-time data to the server, the server analyzes and processes the data sent by the target behavior acquisition device in real time, the server compares the real-time data through a trained abnormal event alarm model, and sends alarm information when the target behavior or track is detected to be abnormal, the pan-tilt camera acquires images of an abnormal event occurrence area and confirms the alarm information, real-time images of roads collected by the pan-tilt camera are stored in the server, the server sends the verified alarm information to a monitoring center, and the monitoring center confirms the alarm information sent by the server and displays the alarm information to send out an early warning to the public or a third-party system.
Further, the target behavior track collection device includes a radar sensor, the radar sensor is disposed on a side of a road, and the radar sensor obtains dynamic information of each target point in a coverage area in real time in a high-frequency scanning manner, including: the real-time speed, the driving direction, the lane, the longitude and latitude information, the target type, the coordinate position relative to the radar sensor, the area and the distribution position are obtained, and the radar sensor sends the collected data information to the server.
Further, the cloud platform camera is evenly spaced and is set up at the road side, and the shooting range border department of adjacent cloud platform camera covers each other, guarantees not to shoot the blind area, the cloud platform camera is connected with the server, and 360 rotatory shootings of cloud platform camera are controlled to the server, and the content of shooing includes video, photo, and the content of shooing sends to the server.
Further, be provided with analysis comparison module, control module, data transmission module, data cache module and the big database of vehicle driving state in the server, the big database of vehicle driving state includes: the speed limit threshold of the current road to the vehicle distinguishes the speed limit of a small passenger car, a large passenger car and a truck; acceleration at the time of acceleration and acceleration at the time of deceleration of a small passenger car, a large passenger car, and a truck; the number of lanes of the current road is used for distinguishing a fast lane, a slow lane and an emergency lane, and each vehicle type corresponds to a driving lane; the corresponding relation between the speed of the vehicles and the number of the vehicles on the road, wherein the average speed of the vehicles with high density is less than that with low density;
the data transmission module receives data monitored by the radar sensor and caches the data in the data caching module, the analysis and comparison module analyzes and compares the data in the normal running state of the vehicle in the large database of the running state of the vehicle with the data monitored by the radar sensor, the analysis and comparison result is transmitted to the background server through the data transmission module, and the control module controls the pan-tilt camera to shoot road pictures.
Furthermore, the analysis and comparison result of the vehicle driving state large database and the corresponding detection data of the radar sensor are input into the analysis and comparison module, the database is perfected, the analysis and comparison module is continuously trained and learned, and the accuracy of analysis and judgment is improved.
Further, the server comprises a plurality of storage units, and the server stores the analysis and comparison result and the road image and sends the analysis and comparison result and the road image to the monitoring center.
Furthermore, the monitoring center comprises a display module and an alarm module, the monitoring center receives the analysis comparison result and the road image sent by the server, the alarm module sends out an alarm according to the analysis comparison result, and the display module pops up a window to display the road image to remind a worker to check and process in time.
Further, the early warning detection system comprises the following steps:
the radar sensor divides a road in a coverage area into a plurality of areas and monitors the running state of a vehicle in each area;
the radar sensor collects real-time motion tracks of all vehicles in each section, and collects vehicle type, moving speed, acceleration, longitude and latitude and direction angle information of each vehicle;
the radar sensor sends the monitored data to the server, detects abnormal deceleration, braking, lane change or density increase of the vehicle in a certain area, and the server analyzes and compares the monitored data with the normal running data of the vehicle in the large database of the running state of the vehicle and judges the type of an abnormal event;
analyzing the running states of all vehicles in the areas adjacent to the areas, and judging whether the abnormal events occurring in the areas are accidental events or not;
the server controls a pan-tilt camera closest to the abnormal event occurrence area to shoot videos and pictures of the abnormal event occurrence area, and the shot road images are transmitted to the server;
the server stores the event time, the event type, the longitude and latitude of the event occurrence position and the road image;
the server transmits the stored information to the monitoring center, the monitoring center gives an alarm and pops up a road image popup window to remind a worker to check and process the information, an abnormal event or an accident in the front area is prompted on an information board in front of the abnormal event occurrence area, the server pays attention to deceleration and avoidance, early warning is carried out, and the accident is reduced.
The embodiment of the invention has the following advantages:
the embodiment of the invention discloses an abnormal event detection and early warning system based on target abnormal behavior track analysis, which monitors the running condition of a vehicle by arranging a radar sensor at the side edge of a road, improves the monitoring precision, sends monitoring data to a server to compare with normal vehicle running data, judges whether an abnormal event occurs or not, judges the type of the event, realizes the primary judgment of the abnormal event, confirms the occurrence of the abnormal event through a road picture shot by a pan-tilt camera, stores a comparison judgment result and a road image by the server, sends an alarm through a monitoring center, displays the road image by popping a window, and carries out early warning in front of an incident area by a worker according to the actual condition so as to reduce the accident occurrence rate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the effects and the achievable by the present invention, should still fall within the range that the technical contents disclosed in the present invention can cover.
Fig. 1 is a flowchart of an abnormal event detection and early warning system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a road abnormal event according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of another road abnormal event according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another road abnormal event according to an embodiment of the present invention;
in the figure: 1-radar sensor, 2-tripod head camera and 3-server.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, the present embodiment discloses an abnormal event detection and early warning system based on target abnormal behavior trajectory analysis, including: the system comprises a target behavior track acquisition device, apan-tilt camera 2 and aserver 3, wherein the target behavior track acquisition device acquires a behavior track of a target and then transmits real-time data to theserver 3, theserver 3 analyzes and processes the data transmitted by the target behavior track acquisition device in real time, theserver 3 compares the real-time data through a trained abnormal event alarm model, and sends alarm information when the target behavior or track is detected to be abnormal, thepan-tilt camera 2 acquires images of an abnormal event occurrence area and confirms the alarm information, real-time images of roads acquired by thepan-tilt camera 2 are stored in the server, theserver 3 transmits the verified alarm information to a monitoring center, and the monitoring center confirms the alarm information transmitted by the server and displays the images to send an early warning to the public or a third-party system.
Target action orbit collection system includes radar sensor 1, radar sensor 1 sets up at the road side, and radar sensor 1 acquires the dynamic information of every target point in the coverage area in real time through the mode of high frequency scanning, includes: the real-time speed, the driving direction, the lane, the longitude and latitude information, the target type, the coordinate position relative to the radar sensor 1, the area and the distribution position, and the radar sensor 1 sends the acquired data information to theserver 3.
Thecloud platform camera 2 is evenly spaced and is set up at the road side, and the shooting range border department of adjacentcloud platform camera 2 covers each other, guarantees not to shoot the blind area,cloud platform camera 2 is connected withserver 3, andserver 3 control cloud platform camera 360 rotatory shootings, and the content of shooing includes video, photo, and the content of shooing sends toserver 3. Be provided with analysis comparison module, control module, data transmission module, data cache module and the big database of vehicle driving state in theserver 3, the big database of vehicle driving state includes: the speed limit threshold of the current road to the vehicle distinguishes the speed limit of a small passenger car, a large passenger car and a truck;
acceleration at the time of acceleration and acceleration at the time of deceleration of a small passenger car, a large passenger car, and a truck;
the number of lanes of the current road is divided into a fast lane, a slow lane and an emergency lane, each vehicle type corresponds to a running lane, a fast lane is used for a passenger car under the normal condition, and a slow lane is used for a truck under the normal condition;
the corresponding relation between the speed of the vehicles and the number of the vehicles on the road, wherein the average speed of the vehicles with high density is less than that with low density;
the data transmission module receives data monitored by the radar sensor 1 and caches the data in the data caching module, the analysis and comparison module analyzes and compares the data of the normal running state of the vehicle in the large vehicle running state database with the data monitored by the radar sensor 1, and the control module controls thepan-tilt camera 2 to shoot road pictures.
Theserver 3 comprises a plurality of storage units, and theserver 3 stores the analysis and comparison result and the road image and sends the analysis and comparison result and the road image to the monitoring center. The monitoring center comprises a display module and an alarm module, the monitoring center receives the analysis comparison result and the road image sent by the server, the alarm module gives an alarm according to the analysis comparison result, and the display module pops up a window to display the road image to remind a worker to check and process in time.
The analysis comparison result of the vehicle driving state big database input analysis comparison module and the corresponding detection data of the radar sensor 1, the database is perfected, the analysis comparison module is continuously trained and learned, the accuracy of analysis and judgment is improved, and abnormal events occurring on the road are judged more quickly.
Example 2
The embodiment discloses an abnormal event detection and early warning system based on target abnormal behavior track analysis, and referring to fig. 2, when a parking event, a throwing event or a falling event occurs in an area C. Influence the normal traffic of rear vehicle, the rear car that comes can select to change the lane and dodge current obstacle, and probably just change the lane and do not slow down. If the track change of a single vehicle is not used as a judgment condition, if the preset continuous vehicle has the behavior at the same time, for example, two connected vehicles change the track in the area, the judgment and output alarm data model set in the comparison database is used for carrying out comparison judgment and analyzing the tracks of the normal vehicle running and the track change behavior, and the behavior is used as a judgment and comparison condition.
Theserver 3 judges that a special event occurs at the position, triggers event alarm and simultaneously drives thepan-tilt camera 2 to shoot videos and pictures of the area where the special event occurs for viewing. Theserver 3 stores the analysis and comparison result and the road image data shot by thepan-tilt camera 2, theserver 3 transmits the stored information to the monitoring center, the monitoring center gives an alarm, pops up a road image popup window to remind a worker to check and process the road image popup window, and the front area is prompted to have an abnormal event or an accident on an information board in front of the abnormal event occurrence area, so that the server pays attention to deceleration and avoidance, early warning is carried out in advance, and the occurrence of the accident is reduced.
Referring to fig. 2, when a parking or a spill, fall event occurs in zone C. The normal passing of the rear vehicle is influenced, and the rear vehicle can be selectively decelerated and changed lanes in the avoiding process. After the radar sensor 1 finds that a single target has slowed down and changed lanes in this area. The radar sensor 1 transmits the lane change data of the vehicle to theserver 3, and theserver 3 compares the lane change data with the normal driving state data of the vehicle in the large database of the driving state of the vehicle to judge that an abnormal event occurs in the current area. At the moment, event alarm can be triggered, and theserver 3 drives thepan-tilt camera 2 to shoot videos and pictures of the special event occurrence area for viewing. Meanwhile, theserver 3 stores event information, theserver 3 transmits the stored information to the monitoring center, the monitoring center gives an alarm and pops up a road image popup window to remind a worker to check and process the image, an information board in front of an abnormal event occurrence area prompts the occurrence of an abnormal event or an accident in the front area, the worker pays attention to deceleration and avoidance, early warning is carried out, and the occurrence of the accident is reduced.
Example 3
The embodiment discloses an abnormal event detection and early warning system based on target abnormal behavior track analysis, and with reference to fig. 3, slow lane running vehicles in a large vehicle running state database are trucks and buses. When no vehicle is close in front of the slow lane in the area C, two or more large vehicles drive into the fast lane at the same time, and a second large vehicle close to the rear of the lane changing vehicle has braking behavior and follows the lane changing vehicle at the same time. There may be a falling obstacle in the current zone C, resulting in a rear multi-car lane change avoidance. The radar sensor 1 transmits the lane change data of the vehicle to theserver 3, and theserver 3 compares the lane change data with the normal driving state data of the vehicle in the large database of the driving state of the vehicle, so that the fact that an abnormal event possibly exists in the area C of the slow lane can be judged.
Theserver 3 triggers event alarm and drives thepan-tilt camera 2 to shoot videos and pictures of the special event occurrence area for viewing. Theserver 3 stores the analysis and comparison result and the road image data shot by thepan-tilt camera 2, theserver 3 transmits the stored information to the monitoring center, the monitoring center gives an alarm, pops up a road image popup window to remind a worker to check and process the road image popup window, and the front area is prompted to have an abnormal event or an accident on an information board in front of the abnormal event occurrence area, so that the server pays attention to deceleration and avoidance, early warning is carried out in advance, and the occurrence of the accident is reduced.
Referring to fig. 3, the vehicle density is greater in the radar detection area C relative to the adjacent two areas B, D, and the average vehicle speed is also lower. The radar sensor 1 transmits the lane change data of the vehicle to theserver 3, and theserver 3 compares the lane change data, the speed data and the vehicle density data with the normal driving state data of the vehicle in the large vehicle driving state database to judge that the current area is abnormal and abnormal events possibly exist.
Theserver 3 triggers event alarm and drives thepan-tilt camera 2 to shoot videos and pictures of the special event occurrence area for viewing. Theserver 3 stores the analysis and comparison result and the road image data shot by thepan-tilt camera 2, theserver 3 transmits the stored information to the monitoring center, the monitoring center gives an alarm, pops up a road image popup window to remind a worker to check and process the road image popup window, and the front area is prompted to have an abnormal event or an accident on an information board in front of the abnormal event occurrence area, so that the server pays attention to deceleration and avoidance, early warning is carried out in advance, and the occurrence of the accident is reduced.
Example 4
The embodiment discloses an abnormal event detection and early warning system based on target abnormal behavior track analysis, and referring to fig. 4, when a region C collapses, all vehicles behind the region C follow the same track to change lanes to avoid the vehicle. Whether an abnormal situation occurs on the road can be judged by theserver 3 analyzing the same or similar tracks of multiple types of vehicles and multiple numbers of vehicles changing lanes.
Theserver 3 triggers event alarm and drives thepan-tilt camera 2 to shoot videos and pictures of the special event occurrence area for viewing. Theserver 3 stores the analysis and comparison result and the road image data shot by thepan-tilt camera 2, theserver 3 transmits the stored information to the monitoring center, the monitoring center gives an alarm, pops up a road image popup window to remind a worker to check and process the road image popup window, and the front area is prompted to have an abnormal event or an accident on an information board in front of the abnormal event occurrence area, so that the server pays attention to deceleration and avoidance, early warning is carried out in advance, and the occurrence of the accident is reduced.
When the preceding vehicle has crawled, but does not meet the crawl criteria. And a plurality of vehicles can avoid the vehicle at the rear, such as deceleration or lane change. At this time, thepan-tilt camera 2 can be driven to check whether the slow vehicle in the current area has an abnormal condition. If abnormal conditions occur, tracking and early warning can be carried out, data are simultaneously sent to theserver 3 for storage, the monitoring center gives an alarm, and real-time video pictures are popped up to remind an operator on duty to check and process.
When a large area parking event occurs in the same area and an area before the area, and a vehicle is not detected in the area after the area, a major accident or event may occur in the area. Theserver 3 drives thepan-tilt camera 2 to check the area and simultaneously sends the data to theserver 3 for storage, the monitoring center gives an alarm, and a real-time video picture is popped up to remind an operator on duty to check and process.
Various vehicle types exist on the highway at the same time, and the time and the form of rapid acceleration and rapid deceleration of a cart and a trolley are obstructed. Whether special events occur in the behavior occurrence area can be judged by judging whether the vehicle is normally accelerated or decelerated and taking the judgment condition of whether the vehicle has track change. At the moment, thepan-tilt camera 2 is driven to check the area, tracking and early warning are carried out, the detection data of the radar sensor 1 and the road picture shot by thepan-tilt camera 2 are simultaneously sent to theserver 3 to be stored and give an alarm, the alarm is carried out at the monitoring center, real-time video playing is carried out, and the watch person is reminded to check and process.
The various scenes can be listed in a vehicle behavior judgment output alarm data model to be stored in a database, and the judgment output alarm data model is self-perfected by continuously learning in the later stage of the system. The method can be used as a new comparison reference object and a judgment output alarm data model, and can quickly make judgment when similar or new abnormal event accident types or abnormal behaviors of the vehicle occur in the future. And the normal behavior data and the abnormal behavior data of the vehicle are continuously improved. The alarm information fed back to the workstation is combined, so that the judgment output alarm data model is continuously learned and improved, the event judgment accuracy can be improved, and the event judgment time can be shortened.
The method and the device can supplement targets of detection type lack, detection content lack and incapability of detection and blind areas existing in the event detection system. The incident detection rate is increased, so that the traffic accident rate on the road can be reduced, the possible risks can be predicted in advance, the driving safety is improved, and the safety and high speed are created.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (5)
1. An abnormal event detection early warning system based on target abnormal behavior track analysis is characterized in that the early warning system comprises: the system comprises a target behavior track acquisition device, a pan-tilt camera and a server, wherein the target behavior track acquisition device acquires a behavior track of a target and then transmits real-time data to the server, the server analyzes and processes the data transmitted by the target behavior acquisition device in real time, the server compares the real-time data through a trained abnormal event alarm model, and sends alarm information when the target behavior or track is detected to be abnormal, the pan-tilt camera acquires images of an abnormal event occurrence area and confirms the alarm information, real-time images of roads acquired by the pan-tilt camera are stored in the server, the server transmits the verified alarm information to a monitoring center, and the monitoring center confirms and displays the alarm information transmitted by the server and sends an early warning to the public or a third-party system;
the cloud deck cameras are uniformly arranged on the side edge of a road at intervals, the boundaries of shooting ranges of adjacent cloud deck cameras are mutually covered, no shooting blind area is guaranteed, the cloud deck cameras are connected with a server, the server controls the cloud deck cameras to shoot in a 360-degree rotating mode, shooting contents comprise videos and pictures, and the shooting contents are sent to the server;
the server is internally provided with an analysis and comparison module, a control module, a data transmission module, a data cache module and a vehicle running state big database, the data transmission module receives data monitored by the radar sensor and caches the data in the data cache module, the analysis and comparison module analyzes and compares the data of the vehicle normal running state in the vehicle running state big database with the data monitored by the radar sensor, an analysis and comparison result is transmitted to the background server through the data transmission module, and the control module controls the pan-tilt camera to shoot road pictures;
the large database of the vehicle running state inputs the analysis and comparison result of the analysis and comparison module and the corresponding detection data of the radar sensor, the database is perfected, the analysis and comparison module is continuously trained and learned, and the accuracy of analysis and judgment is improved;
the early warning detection system comprises the following steps:
the radar sensor divides a road in a coverage area into a plurality of areas and monitors the running state of a vehicle in each area;
the radar sensor collects real-time motion tracks of all vehicles in each section, and collects vehicle type, moving speed, acceleration, longitude and latitude and direction angle information of each vehicle;
the radar sensor sends the monitored data to the server, detects abnormal deceleration, braking, lane change or density increase of the vehicle in a certain area, and the server analyzes and compares the monitored data with the normal running data of the vehicle in the large database of the running state of the vehicle and judges the type of an abnormal event;
analyzing the running states of all vehicles in the areas adjacent to the areas, and judging whether the abnormal events occurring in the areas are accidental events or not;
the server controls a pan-tilt camera closest to the abnormal event occurrence area to shoot videos and pictures of the abnormal event occurrence area, and the shot road images are transmitted to the server;
the server stores the event time, the event type, the longitude and latitude of the event occurrence position and the road image;
the server transmits the stored information to the monitoring center, the monitoring center gives an alarm and pops up a road image popup window to remind a worker to check and process the information, an abnormal event or an accident in the front area is prompted on an information board in front of the abnormal event occurrence area, the server pays attention to deceleration and avoidance, early warning is carried out, and the accident is reduced.
2. The system of claim 1, wherein the target behavior trace collecting device comprises a radar sensor, the radar sensor is disposed at a side of a road, and the radar sensor obtains dynamic information of each target point in a coverage area in real time by means of high-frequency scanning, and the system comprises: the real-time speed, the driving direction, the lane, the longitude and latitude information, the target type, the coordinate position relative to the radar sensor, the area and the distribution position are obtained, and the radar sensor sends the collected data information to the server.
3. The system for detecting and warning abnormal events based on target abnormal behavior track analysis as claimed in claim 1, wherein the large database of vehicle driving states comprises: the speed limit threshold of the current road to the vehicle distinguishes the speed limit of a small passenger car, a large passenger car and a truck; acceleration at the time of acceleration and acceleration at the time of deceleration of a small passenger car, a large passenger car, and a truck; the number of lanes of the current road is used for distinguishing a fast lane, a slow lane and an emergency lane, and each vehicle type corresponds to a driving lane; the corresponding relation between the speed of the vehicles and the number of the vehicles on the road is that the average speed of the vehicles is smaller when the density of the vehicles is high than when the density of the vehicles is low.
4. The system of claim 1, wherein the server comprises a plurality of storage units, and the server stores the analysis and comparison result and the road image and transmits the analysis and comparison result and the road image to the monitoring center.
5. The system of claim 1, wherein the monitoring center comprises a display module and an alarm module, the monitoring center receives the analysis and comparison result and the road image sent by the server, the alarm module sends an alarm according to the analysis and comparison result, and the display module displays the road image through a pop-up window to remind a worker to check and process the road image in time.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910770534.5ACN110473402B (en) | 2019-08-20 | 2019-08-20 | Abnormal event detection and early warning system based on target abnormal behavior trajectory analysis |
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| CN201910770534.5ACN110473402B (en) | 2019-08-20 | 2019-08-20 | Abnormal event detection and early warning system based on target abnormal behavior trajectory analysis |
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| CN110473402A CN110473402A (en) | 2019-11-19 |
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Families Citing this family (42)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325977A (en)* | 2020-02-25 | 2020-06-23 | 创捷运维智能科技有限责任公司 | Tunnel intelligence edge calculation management and control system |
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| CN119252038B (en)* | 2024-12-04 | 2025-03-07 | 河北省交通规划设计研究院有限公司 | Truck abnormal road occupation identification method based on road side millimeter wave radar |
| CN119557819B (en)* | 2025-02-05 | 2025-04-22 | 交通运输部公路科学研究所 | Artificial intelligence-based intelligent car driving data abnormal scene extraction method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102011016217A1 (en)* | 2011-04-06 | 2012-10-11 | Connaught Electronics Ltd. | Method for warning driver of passenger car before rear end collision, involves projecting path of image, and warning driver of vehicle based on image with projected path, where path is determined based on velocity and steering angle |
| US9007197B2 (en)* | 2002-05-20 | 2015-04-14 | Intelligent Technologies International, Inc. | Vehicular anticipatory sensor system |
| CN104952273A (en)* | 2015-07-17 | 2015-09-30 | 东方网力科技股份有限公司 | Warning method, device and system aiming at vehicle behaviors |
| CN106952473A (en)* | 2017-04-01 | 2017-07-14 | 深圳市元征科技股份有限公司 | Road service system detection method and device |
| CN109816982A (en)* | 2019-02-25 | 2019-05-28 | 东南大学 | Attribute correction method of non-motorized vehicle lanes in virtual road network based on shared bicycle trajectory |
| CN109949581A (en)* | 2019-04-11 | 2019-06-28 | 浙江无极互联科技有限公司 | A kind of road condition judgment method based on computer vision |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4061298B2 (en)* | 2004-11-10 | 2008-03-12 | 三菱電機株式会社 | Traffic flow measuring device |
| CN105844904B (en)* | 2016-04-22 | 2019-09-27 | 北京航空航天大学 | A kind of detection of abnormal vehicle behavior and method for tracing based on DSRC |
| CN107742418B (en)* | 2017-09-29 | 2020-04-24 | 东南大学 | Automatic identification method for traffic jam state and jam point position of urban expressway |
| CN108922188B (en)* | 2018-07-24 | 2020-12-29 | 河北德冠隆电子科技有限公司 | Radar tracking and positioning four-dimensional live-action traffic road condition perception early warning monitoring management system |
| CN109212520B (en)* | 2018-09-29 | 2021-04-27 | 河北德冠隆电子科技有限公司 | Road condition sensing abnormal event detection alarm system and method for omnibearing detection radar |
| CN109741600A (en)* | 2018-12-29 | 2019-05-10 | 南京奥杰智能科技有限公司 | Road incidents detection system |
- 2019
- 2019-08-20CNCN201910770534.5Apatent/CN110473402B/enactiveActive
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9007197B2 (en)* | 2002-05-20 | 2015-04-14 | Intelligent Technologies International, Inc. | Vehicular anticipatory sensor system |
| DE102011016217A1 (en)* | 2011-04-06 | 2012-10-11 | Connaught Electronics Ltd. | Method for warning driver of passenger car before rear end collision, involves projecting path of image, and warning driver of vehicle based on image with projected path, where path is determined based on velocity and steering angle |
| CN104952273A (en)* | 2015-07-17 | 2015-09-30 | 东方网力科技股份有限公司 | Warning method, device and system aiming at vehicle behaviors |
| CN106952473A (en)* | 2017-04-01 | 2017-07-14 | 深圳市元征科技股份有限公司 | Road service system detection method and device |
| CN109816982A (en)* | 2019-02-25 | 2019-05-28 | 东南大学 | Attribute correction method of non-motorized vehicle lanes in virtual road network based on shared bicycle trajectory |
| CN109949581A (en)* | 2019-04-11 | 2019-06-28 | 浙江无极互联科技有限公司 | A kind of road condition judgment method based on computer vision |
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