Vehicle tracking based on co-learning particle filter

Authors:
Weilong Ye;Huaping Liu;Fuchun Sun;Meng Gao
Affiliations:
Department of Computer Science and Technology, Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, TNLIST, Beijing, China;Department of Computer Science and Technology, Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, TNLIST, Beijing, China;Department of Computer Science and Technology, Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, TNLIST, Beijing, China;Shijiazhuang Railway Institute, Hebei, China
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

In this paper, we propose a co-learning particle filter approach for vehicle tracking, which is very important for intelligent vehicle. The proposal distribution of the particle filter is a combination of an extra support vector machine (SVM) detector and the motion prior. Previous works focusing on how to online update the detector or the observation likelihood using the tracking results. These approaches belong to "self-learning" fashion and easily tend to drift. The major difference between the proposed approach and previous works is that the SVM detector and the likelihood function can be mutually updated in a co-learning manner. By adopting the co-learning technology, the unlabelled samples which are generated during tracking are utilized to progressively modify the SVM detector and update the observation likelihood; therefore the resulting tracker is more robust and effectively avoids the drift problem. Finally, the performance of the proposed approach is evaluated using extensive real visual tracking examples.