Abstract—In this paper, we propose a robust object tracking
algorithm based on classifier, multi-block and local sparse
coding with multiple discriminative dictionary. The first part of
the propose method is train a classifier with the dictionary
encodes the information of both target information and
background information. The second part exploits the block
information of the object target. The different blocks in a sample
should contribute differently to the visual tracking, the model
effectively exploit the similarity and distinctiveness of different
blocks. Each block is coded on its own discriminative dictionary
to allow flexible coding block and the parameter after sparse
coding can be used for the weights allocation simultaneously.
Furthermore, the update scheme considers both the latest
observations and the original template, thereby enabling to
alleviate drift problem. Extensive experiments on challenging
sequences show that the robust tracking achieved by our
algorithm.
Index Terms—Visual tracking, sparse representation,
multi-block.
Wu Wang, Jinxu Tao, Yongjun Jiang, and Zhongfu Ye are with the
Department of Electronic Engineering and Information Science, University
of Science and Technology of China, National Engineering Laboratory for
Speech and Language Information Processing, China, Hefei, Anhui 230027,
China (e-mail: wangwu6@mail.ustc.edu.cn, jyj365@mail.ustc.edu.cn,
tjingx@ustc.edu.cn, yezf@ustc.edu.cn).
Weiquan Ye is with China Tobacco Anhui Industrial Co., Ltd, Hefei,
Anhui 230088, P. R. China (e-mail: yewq@ustc.edu.cn).
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Cite: Wu Wang, Jinxu Tao, Weiquan Ye, Yongjun Jiang, and Zhongfu Ye, "Robust Object Tracking via Multi-block and Sparsity-Based Representation," International Journal of Knowledge Engineering vol. 1, no. 1, pp. 72-77, 2015.
