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Method and device for detecting road traffic abnormal events in real time

A technology for road traffic and abnormal events, applied in traffic flow detection, closed-circuit television systems, etc., to avoid secondary accidents, reduce work intensity, and reduce traffic delays

Inactive Publication Date: 2014-08-06
TSINGHUA UNIV +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method is simple, efficient, and highly flexible, but it is difficult to accurately describe abnormal behavior through semantics

Method used

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  • Method and device for detecting road traffic abnormal events in real time
  • Method and device for detecting road traffic abnormal events in real time
  • Method and device for detecting road traffic abnormal events in real time

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Embodiment Construction

[0033] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0034] In order to overcome the shortcomings in the prior art that it is difficult to accurately describe abnormal behaviors, and to adapt to the situation where the detection effect of moving objects is unstable due to low image quality, this application aims at the straight lane of the expressway, combining the lane model and the position of the vehicle Semantic modeling of abnormal behavior of vehicles based on speed and speed, and a target detection algorithm based on posterior probability, finally realizing real-time detectio...

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Abstract

The invention provides a method and device for detecting road traffic abnormal events in real time. The method includes the steps of monitoring a road, obtaining a plurality of frames of continuous monitor images, extracting bright white segments from the monitor images, obtaining lane lines and lane end points through processing, building a lane model, determining a bidirectional detection area of a lane according to the lane model, detecting a moving object in the bidirectional detection area according to a Gaussian mixture model background subtraction method, determining the position of the moving object, building the mapping relation between the moving target and an actual vehicle according to the position of the moving target in the multiple frames of continuous monitor images by the adoption of a posterior probability splitting and merging algorithm and a feature point matching and tracking method, obtaining the running track and running speed of the actual vehicle, detecting the lane model and the running track and running speed of the actual vehicle according to a prestored road traffic abnormal behavior semantic model, and judging whether the road traffic abnormal events exist or not. The method has the advantages of being intelligent, high in accuracy and the like.

Description

technical field [0001] The invention belongs to the field of video monitoring, and in particular relates to a real-time detection method and device for abnormal road traffic events. Background technique [0002] The abnormal event detection technology of highway vehicles based on video surveillance can greatly reduce the labor intensity of monitoring personnel, reduce the missed detection rate, improve the real-time detection, and has a good application prospect in highway safety. At present, abnormal behavior detection of traffic vehicles focuses on establishing abnormal behavior models, and analyzing vehicle behavior based on the models. According to the modeling method, it can be divided into three categories, pattern recognition model, state model and semantic model. Pattern recognition establishes vehicle behavior patterns to determine abnormalities through the analysis of vehicle trajectories and behavior characteristics. The algorithm is simple and direct, and the e...

Claims

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Application Information

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IPC IPC(8): G08G1/01H04N7/18
Inventor 刘金江顾明韩军任小斌任磊邱凌云
Owner TSINGHUA UNIV
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