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Computational Intelligence and Neuroscience
Volume 2016, Article ID 5894639, 13 pages
http://dx.doi.org/10.1155/2016/5894639
Research Article

Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking

Department of Automation, Northwestern Polytechnical University, Xi’an 710072, China

Received 10 April 2016; Accepted 10 July 2016

Academic Editor: Ricardo Aler

Copyright © 2016 Honghong Yang and Shiru Qu. 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

Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.