Incremental Discriminative Color Object Tracking

  • Alireza AsvadiEmail author
  • Hami Mahdavinataj
  • Mohammadreza Karami
  • Yasser Baleghi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 427)


This paper presents an object tracking algorithm based on discriminative 3D joint RGB histograms of the object and surrounding background. Mean-shift algorithm on the object confident map is used for localization. An incremental color learning scheme with a forgetting factor is utilized to account for appearance variation of the object. Evaluated against three state of the art methods, experiments demonstrate the effectiveness of the proposed tracking algorithm where the object undergoes variation in illumination and color. Implemented in MATLAB, the proposed tracker runs at 25.7 frames per second.


Visual tracking Color object tracking 3D joint RGB histogram Incremental learning 


  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38, 1–45 (2006)CrossRefGoogle Scholar
  2. 2.
    Cannons, K.: A review of visual tracking. York University, Department of Computer Science and Engineering, Technical report CSE-2008-07 (2008)Google Scholar
  3. 3.
    Yang, H., Shao, L., Zheng, F., Wang, L., Song, Z.: Recent advances and trends in visual tracking: a review. Neurocomputing 74, 3823–3831 (2011)CrossRefGoogle Scholar
  4. 4.
    Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., Hengel, A.V.D.: A survey of appearance models in visual object tracking. ACM Trans. Intell. Syst. Technol. 4, 58:1–58:48 (2013)Google Scholar
  5. 5.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25, 564–577 (2003)CrossRefGoogle Scholar
  6. 6.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2142–2147 (2006)Google Scholar
  7. 7.
    Shahed Nejhum, S.M., Ho, J., Yang, M.H.: Online visual tracking with histograms and articulating blocks. Comput. Vis. Image Und. 114, 901–914 (2010)CrossRefGoogle Scholar
  8. 8.
    He, S., Yang, Q., Lau, R.W., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2427–2434 (2013)Google Scholar
  9. 9.
    Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1631–1643 (2005)CrossRefGoogle Scholar
  10. 10.
    Wei, Y., Sun, J., Tang, X., Shum, H.Y.: Interactive offline tracking for color objects. In: Proceeding of International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  11. 11.
    Wang, D., Lu, H., Xiao, Z., Chen, Y.W.: Fast and effective color-based object tracking by boosted color distribution. Pattern Anal. Appl. 16, 647–661 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Asvadi, A., Karami, M., Baleghi, Y.: Object tracking using adaptive object color modeling. In: Proceeding of 4th Conference on Information and Knowledge Technology, pp. 848–852 (2012)Google Scholar
  13. 13.
    Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29, 261–271 (2007)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77, 125–141 (2008)CrossRefGoogle Scholar
  16. 16.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1619–1632 (2011)CrossRefGoogle Scholar
  17. 17.
    Wu, Y., Lim, J., Yang, M. H.: Online object tracking: a benchmark. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (2013)Google Scholar
  18. 18.
    Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust mean-shift tracking with corrected background-weighted histogram. IET Comput. Vis. 6, 62–69 (2012)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Collins, R.T.: Mean-shift blob tracking through scale space. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. II–234 (2003)Google Scholar
  20. 20.
    Jahandide, H., Mohamedpour, K., Moghaddam, H.A.: A hybrid motion and appearance prediction model for robust visual object tracking. Pattern Recogn. Lett. 33, 2192–2197 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alireza Asvadi
    • 1
    Email author
  • Hami Mahdavinataj
    • 1
  • Mohammadreza Karami
    • 1
  • Yasser Baleghi
    • 1
  1. 1.Babol University of TechnologyBabolIran

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