Soft Computing

, Volume 23, Issue 20, pp 10173–10186 | Cite as

Object tracking via dense SIFT features and low-rank representation

  • Yong Wang
  • Xinbin LuoEmail author
  • Lu Ding
  • Jingjing Wu
Methodologies and Application


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.


Visual object tracking Dense SIFT features Sparse and low-rank constraints Alternating direction method of multipliers 



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).

Compliance with ethical standards

Conflict of interest

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

Ethical standards

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


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  4. 4.School of Mechanical EngineeringJiangnan UniversityWuxiChina

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