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Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

International Journal of Computer Vision volume 111, pages 171–190 (2015)Cite this article

Abstract

Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against \(14\) state-of-the-art tracking methods on a set of \(25\) challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.

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Notes

  1. Generally, the matrix of particle representations is not full-rank. It tends to have a low rank that is usually larger than one.

  2. This follows from the linear representation assumption. Since \(\mathbf {X} = \mathbf {D}\mathbf {Z}\) and \(\mathbf {D}\) can be designed to be an overcomplete full row or column rank matrix, then \(\text {rank}(\mathbf {X})=\text {rank}(\mathbf {Z})\). So, if \(\mathbf {X}\) is low-rank, it follows that \(\mathbf {Z}\) is also low-rank.

  3. The results of Li et al. (2011) are not included in Table 4 since the source code is not available for evaluation and the implementation requires technical details as well as parameter settings not discussed.

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Acknowledgments

This work is supported in part by the research grant for the Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore’s Agency for Science, Technology and Research (A\(^*\)STAR) and NSF CAREER Grant #1149783.

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Tianzhu Zhang

  2. Advanced Digital Sciences Center (ADSC) of the University of Illinois, 1 Fusionopolis Way, #08-10 Connexis North Tower, Singapore, 138632, Singapore

    Tianzhu Zhang & Bernard Ghanem

  3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore

    Si Liu

  4. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Narendra Ahuja

  5. School of Engineering, University of California at Merced, Merced, CA, 95344, USA

    Ming-Hsuan Yang

  6. Department of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Al Khawarizmi Building, Thuwal, 23955-6900, Saudi Arabia

    Bernard Ghanem

Authors
  1. Tianzhu Zhang
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  2. Si Liu
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  3. Narendra Ahuja
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  4. Ming-Hsuan Yang
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Corresponding author

Correspondence to Si Liu.

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Communicated by M. Hebert.

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Zhang, T., Liu, S., Ahuja, N. et al. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning. Int J Comput Vis 111, 171–190 (2015). https://doi.org/10.1007/s11263-014-0738-0

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  • DOI: https://doi.org/10.1007/s11263-014-0738-0

Keywords

  • Visual tracking
  • Temporal consistency
  • Sparse representation
  • Low-rank representation