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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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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DOI: https://doi.org/10.1007/s00500-018-3571-5