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Tracking Using Motion Patterns for Very Crowded Scenes
Xuemei Zhao, Dian Gong, and Gérard Medioni
Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA, 90089, USAxuemeiz@usc.edu
diangong@usc.edu
medioni@usc.edu
Abstract. This paper proposes Motion Structure Tracker (MST) to solve the problem of tracking in very crowded structured scenes. It combines visual tracking, motion pattern learning and multi-target tracking. Tracking in crowded scenes is very challenging due to hundreds of similar objects, cluttered background, small object size, and occlusions. However, structured crowded scenes exhibit clear motion pattern(s), which provides rich prior information. In MST, tracking and detection are performed jointly, and motion pattern information is integrated in both steps to enforce scene structure constraint. MST is initially used to track a single target, and further extended to solve a simplified version of the multi-target tracking problem. Experiments are performed on real-world challenging sequences, and MST gives promising results. Our method significantly outperforms several state-of-the-art methods both in terms of track ratio and accuracy.
Keywords: motion pattern, tracking, very crowded scenes
LNCS 7573, p. 315 ff.
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© Springer-Verlag Berlin Heidelberg 2012