A Bayesian Approach to Tracking Learning Detection

  • Giorgio Gemignani
  • Wongun Choi
  • Alessio Ferone
  • Alfredo Petrosino
  • Silvio Savarese
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


Tracking objects of interest in video sequences, referred in computer vision literature as video tracking or visual tracking, is an essential task for intelligent machines able to understand and react to the surrounding environment. This work investigates the problem of robust, long-term visual tracking of unknown objects in unconstrained environments. Such problem is affected by several challenging difficulties arising from fast camera movements, partial or total object occlusions and temporal disappearance. We describe a novel framework based on Tracking-Learning-Detection (TLD), that combine bayesian optimal filtering with pn on-line learning theory [12] to adapt target visual likelihood during tracking. We designed particle filtering algorithm for parameter inference and propose a solution that enables accurate and efficient tracking. The performance and the long-term stability are demonstrated and evaluated on a set of challenging video sequences usually employed to test tracking algorithms.


Visual tracking MCMC particle filter Adaptive likelihood 


  1. 1.
    Avidan, S.: Ensemble tracking. In: CVPR, pp. 494–501 (2005)Google Scholar
  2. 2.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)CrossRefGoogle Scholar
  3. 3.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, pp. 92–100. ACM, New York (1998)CrossRefGoogle Scholar
  4. 4.
    Yves Bouguet, J.: Pyramidal implementation of the lucas kanade feature tracker. Intel Corporation, Microprocessor Research Labs (2000)Google Scholar
  5. 5.
    Choi, W., Savarese, S.: Multiple target tracking in world coordinate with single, minimally calibrated camera. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 553–567. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-tracking using semi-supervised support vector machines. IEEE Trans. Pattern Anal. Mach. Intell., 1–8 (August 2007)Google Scholar
  7. 7.
    Fan, J., Shen, X., Wu, Y.: Closed-loop adaptation for robust tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 411–424. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Grossberg, S.: Competitive learning: From interactive activation to adaptive resonance. Cognitive Science 11(1), 23–63 (1987)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hoey, J.: Tracking using flocks of features, with application to assisted handwashing. In: British Machine Vision Conference BMVC (2006)Google Scholar
  10. 10.
    Jepson, A.D., Fleet, D.J., El-maraghi, T.F.: Robust online appearance models for visual tracking, pp. 415–422 (2001)Google Scholar
  11. 11.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: Automatic detection of tracking failures. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR 2010, pp. 2756–2759 (2010)Google Scholar
  12. 12.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1409–1422 (2012)CrossRefGoogle Scholar
  13. 13.
    Lepetit, V., Lagger, P., Fua, P.: Randomized trees for real-time keypoint recognition. In: CVPR, pp. 775–781 (2005)Google Scholar
  14. 14.
    Lu, L., Hager, G.D.: A nonparametric treatment for location/segmentation based visual tracking. In: 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, Minnesota, USA, June 18-23, IEEE Computer Society (2007)Google Scholar
  15. 15.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, IJCAI 1981, vol. 2, pp. 674–679. Morgan Kaufmann Publishers Inc., San Francisco (1981)Google Scholar
  16. 16.
    Matthews, I., Ishikawa, T., Baker, S.: The template update problem. IEEE PAMI 26, 810–815 (2003)CrossRefGoogle Scholar
  17. 17.
    Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking (2008)Google Scholar
  18. 18.
    Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forestsGoogle Scholar
  19. 19.
    Salti, S., Cavallaro, A., di Stefano, L.: Adaptive appearance modeling for video tracking: Survey and evaluation. IEEE TIP 21(10), 4334–4348 (2012)Google Scholar
  20. 20.
    Shi, J., Tomasi, C.: Good features to track. In: 1994 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1994), pp. 593–600 (1994)Google Scholar
  21. 21.
    Song, X., Cui, J., Zha, H., Zhao, H.: Vision-based multiple interacting targets tracking via on-line supervised learning. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 642–655. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    Teichman, A., Thrun, S.: Tracking-based semi-supervised learning. Int. J. Rob. Res. 31(7), 804–818 (2012)CrossRefGoogle Scholar
  23. 23.
    Yang, M., Lv, F., Xu, W., Gong, Y.: Detection driven adaptive multi-cue integration for multiple human tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1554–1561. IEEE (2009)Google Scholar
  24. 24.
    Yin, Z., Collins, R.T.: On-the-fly object modeling while tracking. In: CVPR 2007, Minneapolis, Minnesota, USA, June 18-23. IEEE Computer Society (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Giorgio Gemignani
    • 1
  • Wongun Choi
    • 2
  • Alessio Ferone
    • 1
  • Alfredo Petrosino
    • 1
  • Silvio Savarese
    • 2
  1. 1.DSAUniversity of Naples “Parthenope”NapoliItaly
  2. 2.EECSUniversity of MichiganAnn ArborUSA

Personalised recommendations