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Moving vehicle detection in dynamical scene using vector quantization

Moving vehicle detection in dynamical scene is a significant but challenging problem in these days. A new and effective approach to extract moving vehicles is proposed in this paper. In our method, Harris corner and Lucas-Kanade (L-K) optical flow was adopted to generalize feature-point optical flow field between two consecutive frames which obtained from monocular moving camera, and then vector quantization (VQ) was used to cluster the optical flow field using similarity measurement of Euclidean distance and similarity coefficient. At last, through calculating the variance for each class to eliminate the mismatched optical flow and extract the vehicles from background. Experiment result shown that our method has an excellent performance in eliminating the mismatched optical flows. It can extract vehicles from dynamical scene exactly and can also meet the real-time requirement. Furthermore, it provides accurate information for next step of vehicle tracking.