Abstract
Recently video-based data collection has been widely used in intelligent transportation systems. For instance, traffic flow monitoring at an intersection is one of these systems used for transportation network analysis. Main problems in intersection monitoring are vehicle detection and tracking. In this paper, we have developed and implemented a video-based vehicle detection and tracking system. The vehicle detection method is based on a combination of background estimation and dynamic texture modeling. After extracting vehicles from the video frames, a point tracking method has been used for prediction of vehicles' central points in the next frames. Weighted recursive least square has been used for point tracking purpose. Moreover, to solve the occlusion of two or more vehicles problem, fast normalized cross correlation algorithm has been used as a template matching method. The reported experimental results, verify the effectiveness of proposed method in vehicle tracking when occlusions occurs.
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