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)


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].


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.


  1. 1.
    Mei, X., Ling, H.: Robust Visual Tracking and Vehicle Classification via Sparse Representation. TPAMI 33, 2259–2272 (2011)CrossRefGoogle Scholar
  2. 2.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), Article 13 (December 2006) Google Scholar
  3. 3.
    Liu, B., Yang, L., Huang, J., Meer, P., Gong, L., Kulikowski, C.: Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 624–637. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. IJCV 29, 5–28 (1998)CrossRefGoogle Scholar
  5. 5.
    Li, H., Shen, C., Shi, Q.: Real-time visual tracking with compressed sensing. In: CVPR (2011)Google Scholar
  6. 6.
    Black, M.J., Jepson, A.D.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. IJCV 26, 63–84 (1998)CrossRefGoogle Scholar
  7. 7.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. TPAMI 25, 564–575 (2003)CrossRefGoogle Scholar
  8. 8.
    Ross, D., Lim, J., Lin, R.S., Yang, M.H.: Incremental Learning for Robust Visual Tracking. IJCV 77, 125–141 (2008)CrossRefGoogle Scholar
  9. 9.
    Grabner, H., Grabner, M., Bischof, H.: Real-Time Tracking via On-line Boosting. In: BMVC (2006)Google Scholar
  10. 10.
    Avidan, S.: Ensemble tracking. In: CVPR, pp. 494–501 (2005)Google Scholar
  11. 11.
    Leistner, C., Godec, M., Saffari, A., Bischof, H.: On-Line Multi-view Forests for Tracking. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DAGM 2010. LNCS, vol. 6376, pp. 493–502. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)Google Scholar
  13. 13.
    Doucet, A., De Freitas, N., Gordon, N.: Sequential monte carlo methods in practice. Springer, New York (2001)zbMATHGoogle Scholar
  14. 14.
    Yang, C., Duraiswami, R., Davis, L.: Fast multiple object tracking via a hierarchical particle filter. In: ICCV (2005)Google Scholar
  15. 15.
    Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2042–2049 (2012)Google Scholar
  16. 16.
    Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via structured multi-task sparse learning. To appear in IJCV (2012)Google Scholar
  17. 17.
    Peng, Y., Ganesh, A., Wright, J., Xu, W., Ma, Y.: RASL: Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2010)Google Scholar
  18. 18.
    Liu, G., Lin, Z., Yu, Y.: Robust subspace segmentation by low-rank representation. In: ICML (2010)Google Scholar
  19. 19.
    Candès, E., Li, X., Ma, Y., Wright, J.: Robust Principal Component Analysis? Journal of the ACM 58 (2011)Google Scholar
  20. 20.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. TPAMI 31, 210–227 (2009)CrossRefGoogle Scholar
  21. 21.
    Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR, pp. 1269–1276 (2010)Google Scholar
  22. 22.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR, pp. 798–805 (2006)Google Scholar
  23. 23.
    Lin, Z., Ganesh, A., Wright, J., Wu, L., Chen, M., Ma, Y.: Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. Technical Report UILU-ENG-09-2214, UIUC (August 2009)Google Scholar

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

Personalised recommendations