Visual tracking using iterative sparse approximation

  • Authors:
  • Huaping Liu;Fuchun Sun;Meng Gao

  • Affiliations:
  • Department of Computer Science and Technology, Tsinghua University, P.R. China and State Key Laboratory of Intelligent Technology and Systems, Beijing, P.R. China;Department of Computer Science and Technology, Tsinghua University, P.R. China and State Key Laboratory of Intelligent Technology and Systems, Beijing, P.R. China;Shijiazhuang Tiedao University, Hebei Province, China

  • Venue:
  • ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
  • Year:
  • 2011

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Abstract

Recent research has advocated the use of sparse representation for tracking objects instead of the conventional histogram object representation models used in popular algorithms. In this paper we propose a new tracker. The core is that the tracking results is iteratively updated by gradually optimizing the sparsity and reconstruction error. The effectiveness of the proposed approach is demonstrated via comparative experiments.