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Object tracking via dense SIFT features and low-rank representation

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Abstract

In this paper, we present a low-rank sparse tracking method which builds upon the particle filtering framework. The proposed method learns the local dense scale-invariant feature transform features corresponding to candidate samples jointly by exploiting the underlying sparse and low-rank constraints. Furthermore, the alternating direction method of multipliers method guarantees the optimization equation can be solved accurately and robustly. We evaluate our proposed tracking method against 9 state-of-the-art trackers on a set of 64 challenging sequences. Experimental results show that the proposed method performs favorably against state-of-the-art trackers in terms of accuracy.

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Acknowledgements

This paper is jointly supported by the National Natural Science Foundation of China (61305016) and Fundamental Research Funds for the Central Universities (Grant No. JUSRP1059).

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Yong Wang

  2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xinbin Luo

  3. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Lu Ding

  4. School of Mechanical Engineering, Jiangnan University, Wuxi, China

    Jingjing Wu

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  1. Yong Wang
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  2. Xinbin Luo
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  3. Lu Ding
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  4. Jingjing Wu
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Correspondence to Xinbin Luo.

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The authors declare that they have no conflict of interest regarding the publication of this paper.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Wang, Y., Luo, X., Ding, L. et al. Object tracking via dense SIFT features and low-rank representation. Soft Comput 23, 10173–10186 (2019). https://doi.org/10.1007/s00500-018-3571-5

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