Detection method for abnormal behavior of vehicle based on spectrum clustering

Abstract

The invention discloses a detection method for an abnormal behavior of a vehicle based on spectrum clustering. The detection method comprises the following steps: obtaining a space-time track of a moving target through video tracking; removing abnormality and preprocessing, thereby obtaining a normal track; constructing an image for the track, thereby obtaining an undirected image corresponding to a track sequence; calculating similarity among tracks, thereby obtaining a similarity matrix; performing Laplace transformation on the similarity matrix, thereby obtaining a Laplace matrix; clustering the feature vector matrix of the front k maximal feature values; after performing mode learning on a motion track, obtaining motion modes of the target under a normal state; if a new track meets one of the motion modes, i.e. a normal motion mode, confirming that the traffic is normal; and if not, confirming that the vehicle abnormally runs, namely, the traffic abnormality occurs. According to the detection method, through the clustering learning for the vehicle track, the monitoring for the abnormal behavior of the vehicle is realized, the abnormal lane change is detected and the basis for automation of traffic management is supplied.

Description

technical field [0001] The invention relates to a behavior detection method of a moving vehicle, in particular to a method for detecting abnormal behavior of a moving vehicle by performing pattern learning on the trajectory of the moving vehicle, and belongs to the field of moving object detection. Background technique [0002] With the rapid development of the economy, the number of vehicles has increased sharply, leading to an increase in road traffic accidents, resulting in an increase in casualties and economic losses year by year. Among them, traffic accidents caused by various traffic violations account for more than 80% of the total traffic accidents. [0003] With the continuous development of sensor technology and computer technology, using traffic video surveillance devices to detect, identify and deal with traffic violations has achieved good results in practical applications. [0004] In the prior art, traffic video surveillance devices can be used to automatica...

AI Technical Summary

Problems solved by technology

However, due to the diversity and complexity of the distribution of sample data sets in traffic behavior monitoring, conventional extraction methods cannot express the similarity between trajectories well, which makes trajectory recognition difficult to be practically applied.

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