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A vision-based vehicle detection method is presented in this paper. The proposed method is composed of two steps, i.e., hypothesis generation and hypothesis verification. An adaptive background modeling and updating method is proposed to detect foreground regions in video sequences. With the prior knowledge of the vehicle appearance, the possible vehicle locations are extracted from the foreground regions and the touched vehicles are separated. Finally, hypothesized regions are verified by comparing their appearances with vehicle model. The performance of the proposed method is verified on videos captured under versatile conditions, and good results are achieved even in heavy traffic conditions.