Intelligent traffic video surveillance system, including vehicle inspection, vehicle tracking and license plate recognition module. The video vehicle detection in this paper is divided into four steps: video image pre-processing, background modeling and updating, foreground detection and adhesion vehicle area segmentation. After the collected video sequence is subjected to mean-value down-sampling and gray-scale conversion preprocessing, the background model is built using a mixture of Gaussian models. A sliding window is set on the time axis, and the grayscale video frame image in the sliding window is median-filtered to output a background gradient map. By merging the results of the spatial background difference between the color space and the grayscale gradient, the foreground image detected by the vehicle is obtained. Finally, the background difference is obtained in the gradient domain to obtain the foreground image to achieve vehicle detection.
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