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The Scientific World Journal
Volume 2014, Article ID 402185, 13 pages
http://dx.doi.org/10.1155/2014/402185
Research Article

Part-Based Visual Tracking via Online Weighted P-N Learning

1College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2College of Science, Huazhong Agricultural University, Wuhan 430070, China
3Department of Physics, Central China Normal University, Wuhan 430079, China
4School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Received 11 April 2014; Accepted 11 June 2014; Published 15 July 2014

Academic Editor: Yu-Bo Yuan

Copyright © 2014 Heng Fan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.