Abstract
Video cameras play an important role in achieving the potentials promised by Intelligent Transportation Systems. In particular, video tracking systems have been widely applied in traffic flow data collection and incident detection by tracking and analyzing vehicle trajectories through video image processing. Focusing on current vehicle tracking methods that are limited to complex traffic scenes such as target non-uniqueness and color distraction in real traffic situations, this paper proposes an improved Continuously Adaptive Mean Shift (CAMShift) method for automatic multi-vehicle tracking using video cameras. The proposed method firstly uses a background subtraction method for automatically detecting, selecting, and initializing targets, i.e., vehicles. Moreover, a motion estimation method is proposed for estimating vehicle motion states and predicting the new locations of the vehicles. Finally, both the color and edge feature distributions of the vehicles are extracted and a mixed model is established for searching the matching vehicles in the next image frame. The proposed method was comparatively evaluated with the traditional CAMShift method using the same video sequences captured from real traffic situations. Evaluation results showed that the proposed method is efficient for accurately tracking vehicle movements, and is promising for practical applications.
Published Version
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