Semi-supervised particle filter for visual tracking

Authors:
Huaping Liu;Fuchun Sun
Affiliations:
Department of Computer Science and Technology, Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, Beijing, P.R.China;Department of Computer Science and Technology, Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, Beijing, P.R.China
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Abstract

In this paper, a semi-supervised particle filter approach is proposed for visual tracking. The combination of semi-supervised learning and particle filter is very natural since the unlabelled samples are generated by particle propagation. In addition, the proposed semi-supervised particle filter can online select different features for robust tracking. To the best knowledge of the authors, this is the first time for the semi-supervised learning technology to be incorporated into the framework of particle filter. Finally, the performance of the proposed approach is evaluated using real visual tracking examples.