Vehicle monitoring is a very important part in the intelligent transportation systems towards real-time monitoring of intersection traffic condition, the dynamic traffic incident detection and traffic parameter extraction. This paper proposes a vehicle tracking method based on mean shift. During the detection period, tracking objects of vehicles are constructed. The current vehicle position is predicted from the target area of former frame. In the candidate area of the target image, foreground area mask is adopted as a condition whether a pixel is selected; this makes the colour probability density to more accurately reflect the characteristics of the vehicle, and avoids the background region's influence on the mean shift iterations. Experiments show that this method can effectively detect the position of the vehicle, and provides an effective vehicle tracking method in the intelligent transportation system.
As urban road intersections are prone to traffic congestion and traffic accidents, monitoring the crossing of vehicles and predicting the state is needed to reduce traffic congestion, regulate driver behavior and prevent accidents. Background subtraction and mean shift tracking are used to track vehicles. The whole monitoring process is as following. Firstly, secondary selected strategy is used to construct background model. Then vehicle tracking objects are built at the trigger area of detection by the background subtraction. Finally, the mean shift algorithm is utilized to track vehicles. The secondary selected strategy is a new algorithm designed in this article .It can reconstruct quickly the accurate background from the crowd video frames. Using background subtraction can eliminate the interference of background on the color probability density of target in mean shift algorithm. The whole algorithm achieves the real-time tracking in complicated situation in a high accuracy.
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