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Fast and effective color-based object tracking by boosted color distribution

Abstract

In this paper, we propose a novel tracking algorithm, boosted color distribution (BCD), for tracking color objects. There exist three contributions in this paper. First, we propose a novel online gentle boost (OGB) algorithm for online learning. The essential idea of OGB is composed of two aspects: online updating candidate weak classifiers, and then choosing and combining them in a boosting way. Second, we design a novel weak classifier, log color feature distribution ratio, which focuses on the difference of color distributions rather than individual samples and provides a simple yet effective manner of mining color features for object tracking. Third, by combining our OGB algorithm and our log color features, we develop a fast yet effective color-based object tracking algorithm. Numerous experiments demonstrate that our tracking algorithm is better than or not worse than some state-of-the-art tracking algorithms on some public sequences.Overall, this paper presents a novel BCD algorithm for color object tracking that achieves good results at a fast speed.

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Notes

  1. 1.

    http://www.cs.technion.ac.il/~amita/fragtrack/fragtrack.htm.

  2. 2.

    http://www.cvg.rdg.ac.uk/pets2001/.

  3. 3.

    http://www-prima.imag.fr/PETS04/index.html.

  4. 4.

    http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/.

  5. 5.

    http://www.eecs.qmul.ac.uk/~andrea/spevi.html.

  6. 6.

    http://www.cs.technion.ac.il/~amita/fragtrack/fragtrack.htm.

  7. 7.

    http://www.cs.toronto.edu/~dross/ivt/.

  8. 8.

    http://www.vision.ee.ethz.ch/boostingTrackers/onlineBoosting.htm.

  9. 9.

    http://www.vision.ee.ethz.ch/boostingTrackers/semiBoosting.htm.

  10. 10.

    http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml.

  11. 11.

    http://www4.comp.polyu.edu.hk/~cskhzhang/trackingcodes.htm.

  12. 12.

    http://whluo.net/category/code_software/.

References

  1. 1.

    Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 798–805

  2. 2.

    Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072

  3. 3.

    Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271

  4. 4.

    Babenko B, Yang MH, Belongie S (2009) Visual tracking with online multiple instance learning. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 983–990

  5. 5.

    Black MJ, Jepson AD (1998) EigenTracking : robust matching and tracking of articulated objects using a view-based representation. Int J Comput Vis 26(1):63–84

  6. 6.

    Collins RT, Liu Y, Leordeanu M (2005) Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell 27(10):1631–1643

  7. 7.

    Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid cbjects using mean Shift. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 142–149

  8. 8.

    Friedman J, Hastie T, Tibshirani R (2009) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337–407

  9. 9.

    Grabner H, Bischof H (2006) On-line boosting and vision. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 260–267

  10. 10.

    Grabner H, Grabner M, Bischof H (2006) Real-time tracking via on-line boosting. In: Proceedings of British machine vision conference, pp 47–56

  11. 11.

    Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. In: Proceedings of European conference on computer vision, pp 234–247

  12. 12.

    Jepson AD, Fleet DJ, El-Maraghi TF (2003) Robust online appearance models for visual tracking. IEEE Trans Pattern Anal Mach Intell 25(10):296–1311

  13. 13.

    Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1822–1829

  14. 14.

    Kalal Z, Matas J, Mikolajczyk K (2010) P-N learning: bootstrapping binary classifiers by structural constraints. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 49–56

  15. 15.

    Lin RS, Ross DA, Lim J, Yang MH (2004) Adaptive discriminative generative model and its applications. In: Advances in neural information processing systems

  16. 16.

    Liu R, Cheng J, Lu H (2009) A robust boosting tracker with minimum error bound in a co-training framework. In: ICCV, pp 1459–1466

  17. 17.

    Lu H, Lu S, Wang D, Wang S, Leung H (2012) Pixel-wise spatial pyramid-based hybrid tracking. IEEE Trans Circuits Syst Video Technol 22(9):1365–1376

  18. 18.

    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

  19. 19.

    Parag T, Porikli F, Elgammal AM (2008) Boosting adaptive linear weak classifiers for online learning and tracking. In: CVPR, pp 1–8

  20. 20.

    Ross D, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141

  21. 21.

    Santner J, Leistner C, Saffari A, Pock T, Bischof H (2010) PROST: parallel robust online simple tracking. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 723–730

  22. 22.

    Stern H, Efros B (2002) Adaptive color space switching for face tracking in multi-colored lighting environments. In: Proceedings of IEEE international conference on automatic face and gesture recognition, pp 249–254

  23. 23.

    Tang F, Brennan S, Zhao Q, Tao H (2007) Co-tracking using semi-supervised support vector machines. In: Proceedings of the IEEE international conference on computer vision, pp 1–8

  24. 24.

    Tian M, Zhang W, Liu F (2007) On-line ensemble SVM for robust object tracking. In: Asian conference on computer vision, pp 355–364

  25. 25.

    Viola PA, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 511–518

  26. 26.

    Wang D, Lu H, Li X (2011) Two dimensional principal components of natural images and its application. Neurocomputing 74(17):2745–2753

  27. 27.

    Wang D, Lu H, Yang MH (2013) Least soft-threshold squares tracking. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2371–2378

  28. 28.

    Wang D, Lu H, Yang MH (2013) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325

  29. 29.

    Wang S, Lu H, Yang F, Yang MH (2011) Superpixel tracking. In: Proceedings of the IEEE international conference on computer vision, pp 1323–1330

  30. 30.

    Wei Y, Sun J, Tang X, Shum HY (2007) Interactive offline tracking for color objects. In: Proceedings of the IEEE international conference on computer vision, pp 1–8

  31. 31.

    Zhong W, Lu H, Yang MH (2012) Robust object tracking via sparsity-based collaborative model. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1838–1845

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Correspondence to Huchuan Lu.

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Wang, D., Lu, H., Xiao, Z. et al. Fast and effective color-based object tracking by boosted color distribution. Pattern Anal Applic 16, 647–661 (2013). https://doi.org/10.1007/s10044-013-0347-5

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Keywords

  • Visual tracking
  • Color object tracking
  • Online gentle boost
  • Boost color distribution
  • Scale handling