Urban traffic illegal behavior detection method based on video monitoring system

Abstract

The invention discloses an urban traffic illegal behavior detection method based on a video monitoring system. The urban traffic illegal behavior detection method based on the video monitoring system includes the following steps of trajectory extraction, trajectory structuring, trajectory similarity calculation, trajectory clustering and modeling and abnormality detection, wherein in the trajectory extraction step, a video movement target is detected and tracked to extract a target trajectory; in the trajectory structuring step, a trajectory section is segmented and structured, and the trajectory section is represented through four structural characteristics; in the trajectory similarity calculation step, the characteristic distances corresponding to the four structural characteristics of the trajectory section are calculated respectively, and the similarity between trajectories is calculated through weighing and calculation of the relative similarity between the trajectories; in the trajectory clustering and modeling step, a similarity matrix is structured according to the similarity between the trajectories, the trajectories are clustered, the clustered trajectories are built into Gaussian model sets, and the trajectories belonging to the same class are built into one same set of Gaussian models; in the abnormality detection step, the probability of a trajectory belonging to each model is calculated, and abnormality is judged according to whether the largest probability is larger than a preset threshold or not. According to the method, traffic illegal behaviors are detected based on the video monitoring system, and the efficiency and the accuracy of detection and the illegal behavior class are improved.

Description

technical field [0001] The invention belongs to the field of machine vision behavior analysis, and in particular relates to a method for detecting urban traffic violations based on a video monitoring system. Background technique [0002] As of the end of 2012, the number of automobiles in China has exceeded 120 million. The rapid increase in the number of vehicles has led to an increase in traffic violations and frequent traffic accidents on the road, resulting in a large number of casualties and huge economic losses. At present, traffic management departments can automatically detect some traffic violations of vehicles by using video surveillance systems, such as speeding, illegal parking and reverse driving. However, for other illegal behaviors such as illegal lane changes, as well as abnormal behaviors of other traffic participants, including pedestrians or bicycles appearing in motor vehicle lanes, pedestrians crossing the road and other violations that may cause traffic...

AI Technical Summary

Problems solved by technology

However, in the current trajectory-based abnormal behavior detection methods, most of them regard the trajectory as a simple set of spatio-temporal points, ignoring the rich information contained in the trajectory, resulting in poor scene analysis effect and difficult to be used in practice. application

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