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Signal, Image and Video Processing

, Volume 8, Issue 6, pp 1059–1068 | Cite as

Object tracking with sparse representation and annealed particle filter

  • Xiangyang Wang
  • Ying Wang
  • Wanggen Wan
  • Jenq-Neng Hwang
Original Paper

Abstract

Recently, the L1 tracker is proposed for robust visual tracking. However, L1 tracker is still in traditional particle filter framework. As we know, particle filters suffer from some problems such as sample impoverishment. In this paper, we propose a new visual tracking algorithm, sparse representation based annealed particle filter, to further improve the performance of L1 tracker. As in L1 tracker, we find the tracking target at a new frame by sparsely representing each target candidate with both target and trivial templates. The sparsity is achieved by solving an \(\ell _{1}\)-regularized least squares problem. The candidate with the largest likelihood is taken as the tracking target. But different from L1 tracker, instead of tracking objects in the common particle filter framework, we solve the sparse representation problem in an annealed particle filter (APF) framework. In the APF framework, the sampling covariance and annealing factors are incorporated into the tracking process. The annealing strategy can achieve “smart sampling” to avoid generating invalid particles corresponding to infeasible targets. Both qualitative and quantitative evaluations on challenging video sequences are implemented to demonstrate the favorable performance in comparison with several other state-of-the-art tracking schemes.

Keywords

Visual tracking Sparse representation Annealed particle filter \(\ell _{1}\)-Minimization 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC, No. 60975024), the Shanghai Natural Science Foundation (No. 09ZR1412300), and National High Technology Research and Development Program of China (863 Program, Grant No. 2013AA01A603).

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Xiangyang Wang
    • 1
    • 2
  • Ying Wang
    • 1
    • 2
  • Wanggen Wan
    • 1
    • 2
  • Jenq-Neng Hwang
    • 2
    • 3
  1. 1.School of Communication and Information EngineeringShanghai UniversityShanghaiChina
  2. 2.Institute of Smart CityShanghai UniversityShanghaiChina
  3. 3.Department of Electrical EngineeringUniversity of WashingtonSeattleUSA

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