Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise

  • Yuefeng Chen
  • Qing Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)


Recently a category of tracking methods based on “tracking-by-detection” is widely used in visual tracking problem. Most of these methods update the classifier online using the samples generated by the tracker to handle the appearance changes. However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples. In this paper, we split the class labels into true labels and noise labels and model them by sparse representation. A novel dynamic classifier selection method, robust to noisy training data, is proposed. Moreover, we apply the proposed classifier selection algorithm to visual tracking by integrating a part based online boosting framework. We have evaluated our proposed method on 12 challenging sequences involving severe occlusions, significant illumination changes and large pose variations. Both the qualitative and quantitative evaluations demonstrate that our approach tracks objects accurately and robustly and outperforms state-of-the-art trackers.


Class Label Sparse Representation Appearance Model Visual Tracking Multiple Instance Learning 
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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  1. 1.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR, vol. (1), pp. 798–805 (2006)Google Scholar
  2. 2.
    Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29, 261–271 (2007)CrossRefGoogle Scholar
  3. 3.
    Bai, T., Li, Y.F.: Robust visual tracking with structured sparse representation appearance model. Pattern Recognition 45, 2390–2404 (2012)zbMATHCrossRefGoogle Scholar
  4. 4.
    Babenko, B., Yang, M.H., Belongie, S.J.: Visual tracking with online multiple instance learning. In: CVPR, pp. 983–990 (2009)Google Scholar
  5. 5.
    Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust ℓ1 tracker using accelerated proximal gradient approach. In: CVPR (2012)Google Scholar
  6. 6.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25, 564–575 (2003)CrossRefGoogle Scholar
  7. 7.
    Everingham, M., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88, 303–338 (2010)CrossRefGoogle Scholar
  8. 8.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: CVPR, vol. (1), pp. 260–267 (2006)Google Scholar
  9. 9.
    Grabner, H., Leistner, C., Bischof, H.: Semi-supervised On-Line Boosting for Robust Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Gong, H., Sim, J., Likhachev, M., Shi, J.: Multi-hypothesis motion planning for visual object tracking. In: ICCV, pp. 619–626 (2011)Google Scholar
  11. 11.
    Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: ICCV, pp. 263–270 (2011)Google Scholar
  12. 12.
    Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)Google Scholar
  13. 13.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: Bootstrapping binary classifiers by structural constraints. In: CVPR, pp. 49–56 (2010)Google Scholar
  14. 14.
    Ko, A.H.R., Sabourin, R., de Souza Britto Jr., A.: From dynamic classifier selection to dynamic ensemble selection. Pattern Recognition 41, 1718–1731 (2008)zbMATHCrossRefGoogle Scholar
  15. 15.
    Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR, pp. 1269–1276 (2010)Google Scholar
  16. 16.
    Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: ICCV, pp. 1195–1202 (2011)Google Scholar
  17. 17.
    Li, G., Qin, L., Huang, Q., Pang, J., Jiang, S.: Treat samples differently: Object tracking with semi-supervised online covboost. In: ICCV, pp. 627–634 (2011)Google Scholar
  18. 18.
    Li, X., Shen, C., Shi, Q., Dick, A., van den Hengel, A.: Non-sparse linear representations for visual tracking with online reservoir metric learning. In: CVPR (2012)Google Scholar
  19. 19.
    Liu, B., Huang, J., Yang, L., Kulikowski, C.A.: Robust tracking using local sparse appearance model and k-selection. In: CVPR, pp. 1313–1320 (2011)Google Scholar
  20. 20.
    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
  21. 21.
    Mei, X., Ling, H.: Robust visual tracking using ℓ1 minimization. In: ICCV, pp. 1436–1443 (2009)Google Scholar
  22. 22.
    Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. International Journal of Computer Vision 77, 125–141 (2008)CrossRefGoogle Scholar
  23. 23.
    Stalder, S., Grabner, H., van Gool, L.: Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition. In: ICCV Workshops, pp. 1409–1416 (2009)Google Scholar
  24. 24.
    Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. (1), pp. 511–518 (2001)Google Scholar
  25. 25.
    Viola, P.A., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: NIPS (2005)Google Scholar
  26. 26.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)CrossRefGoogle Scholar
  27. 27.
    Wu, Y., Ling, H., Yu, J., Li, F., Mei, X., Cheng, E.: Blurred target tracking by blur-driven tracker. In: ICCV, pp. 1100–1107 (2011)Google Scholar
  28. 28.
    Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: CVPR (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yuefeng Chen
    • 1
  • Qing Wang
    • 1
  1. 1.School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anP.R. China

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