Tracking using motion patterns for very crowded scenes

Authors:
Xuemei Zhao;Dian Gong;Gérard Medioni
Affiliations:
Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA;Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA;Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Year:
2012

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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.