On-Line Ensemble SVM for Robust Object Tracking

  • Min Tian
  • Weiwei Zhang
  • Fuqiang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)


In this paper, we present a novel visual object tracking algorithm based on ensemble of linear SVM classifiers. There are two main contributions in this paper. First of all, we propose a simple yet effective way for on-line updating linear SVM classifier, where useful “Key Frames” of target are automatically selected as support vectors. Secondly, we propose an on-line ensemble SVM tracker, which can effectively handle target appearance variation. The proposed algorithm makes better usage of history information, which leads to better discrimination of target and the surrounding background. The proposed algorithm is tested on many video clips including some public available ones. Experimental results show the robustness of our proposed algorithm, especially under large appearance change during tracking.


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  1. 1.
    Hager, G.D., Belhumeur, P.N.: Efficient region tracking with parametric models of geometry and illumination. PAMI 20(10), 1025–1039 (1998)Google Scholar
  2. 2.
    Black, M.J., Jepson, A.: EigenTracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision 26(1), 63–84 (1998)CrossRefGoogle Scholar
  3. 3.
    Isard, M., Blake, A.: Condensation-Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  4. 4.
    Perez, P., et al.: Color-Based Probabilistic Tracking. In: ECCV, pp. 661–675 (2002)Google Scholar
  5. 5.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24, 971–987 (2002)Google Scholar
  6. 6.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. PAMI 25(5), 564–577 (2003)Google Scholar
  7. 7.
    Vacchetti, L., Lepetit, V., Fua, P.: Fusing online and offline information for stable 3D tracking in real-time. In: CVPR 2003, vol. 2, pp. 241–248 (2003)Google Scholar
  8. 8.
    Nummiaroa, K., Koller-Meierb, E., Gool, L.V.: An Adaptive Color-Based Particle Filter. Image and Vision Computing 99–110 (2003)Google Scholar
  9. 9.
    Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. PAMI 25(10), 1296–1311 (2003)Google Scholar
  10. 10.
    Avidan, S.: Support Vector Tracking. PAMI 26(8), 1064–1072 (2004)Google Scholar
  11. 11.
    Matthews, I., Ishikawa, T., Baker, S.: The Template Update Problem. PAMI 26, 810–815 (2004)Google Scholar
  12. 12.
    Okuma, K., Taleghani, A.: A Boosted Particle Filter: Multitarget Detection and Tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 28–39. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Ross, D., Lim, J., Yang, M.H.: Probabilistic visual tracking with incremental subspace update. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 470–482. Springer, Heidelberg (2004)Google Scholar
  14. 14.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  15. 15.
    Porikli, F.: Integral histogram: a fast way to extract histograms in Cartesian spaces. In: CVPR 2005, vol. 1, pp. 829–836 (2005)Google Scholar
  16. 16.
    Avidan, S.: Ensemble tracking. In: Proceedings of CVPR 2005. vol.2, pp. 494–501 (2005)Google Scholar
  17. 17.
    Grabner, H., Bischof, H.: On-line Boosting and Vision. In: CVPR 2006, vol. 1, pp. 260–267 (2006)Google Scholar
  18. 18.
    Wu, Y., Huang, T.S.: Color Tracking by Transductive Learning. In: Proceedings of CVPR 2000, vol. 1, pp. 133–138 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Min Tian
    • 1
  • Weiwei Zhang
    • 2
  • Fuqiang Liu
    • 1
  1. 1.Broadband Wireless Communication and Multimedia Laboratory, Tongji University, ShanghaiChina
  2. 2.Microsoft Research Asia, BeijingChina

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