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Low-Rank Sparse Learning for Robust Visual Tracking

  • Tianzhu Zhang
  • Bernard Ghanem
  • Si Liu
  • Narendra Ahuja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1].

Keywords

Sparse Representation Object Tracking Visual Tracking Template Size Background Template 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tianzhu Zhang
    • 1
  • Bernard Ghanem
    • 2
    • 1
  • Si Liu
    • 3
  • Narendra Ahuja
    • 1
    • 4
  1. 1.Advanced Digital Sciences Center of UIUCSingapore
  2. 2.King Abdullah University of Science and TechnologySaudi Arabia
  3. 3.ECE DepartmentNational University of SingaporeSingapore
  4. 4.University of Illinois at Urbana-ChampaignUrbanaUSA

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