Abstract:

The vehicle lane model is the base of vehicle tracking and vehicle behavior analysis. However, it is difficult to establish vehicle lane model through lane detection algorithm because it is dark on the highway or urban road, and it is difficult to track vehicle exactly when the video frame rate is slow or the vehicle’s speed is too fast. Therefore, the learning based vehicle lane model establishment method and the multi frames based best matching tracking method are proposed. Firstly, a fast brightobject segmentation process based on automatic multilevel histogram threshold is applied to extract bright objects effectively. Secondly, some novehicle brightregions are removed by some features of vehicle's lamps. What’s more, the lamps are clustered into a car object by using spatial information and tracked by the multi frame based best matching tracking method. Finally, tracking information and vehicle lane mode are used to analyze abnormal events. Experimental results show that the algorithm can exactly detect abnormal events such as changing lane, reverse driving, heavy traffic, stopping car etc at night and it has strong robustness.

Key words: traffic surveillance;vehicle detection;vehicle tracking;abnormal events analysis;vehicle lane model