Multimedia Tools and Applications

, Volume 76, Issue 2, pp 2039–2057 | Cite as

Robust object tracking based on sparse representation and incremental weighted PCA

  • Xiaofen Xing
  • Fuhao Qiu
  • Xiangmin Xu
  • Chunmei Qing
  • Yinrong Wu
Article

Abstract

Object tracking plays a crucial role in many applications of computer vision, but it is still a challenging problem due to the variations of illumination, shape deformation and occlusion. A new robust tracking method based on incremental weighted PCA and sparse representation is proposed. An iterative process consisting of a soft segmentation step and a foreground distribution update step is adpoted to estimate the foreground distribution, cooperating with incremental weighted PCA, we can get the target appearance in terms of the PCA components with less impact of the background in the target templates. In order to make the target appearance model more discriminative, trivial and background templates are both added to the dictionary for sparse representation of the target appearance. Experiments show that the proposed method with some level of background awareness is robust against illumination change, occlusion and appearance variation, and outperforms several latest important tracking methods in terms of tracking performance.

Keywords

Tracking Sparse representation Incremental weighted PCA 

References

  1. 1.
    Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072CrossRefGoogle Scholar
  2. 2.
    Babenko B, Yang M-H, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632CrossRefGoogle Scholar
  3. 3.
    Bao C, Wu Y, Ling H, Ji H (2012) Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1830–1837Google Scholar
  4. 4.
    Cehovin L, Kristan M, Leonardis A (2011) An adaptive coupled-layer visual model for robust visual tracking. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 1363–1370Google Scholar
  5. 5.
    Cehovin L, Kristan M, Leonardis A (2013) Robust visual tracking using an adaptive coupled-layer visual model. IEEE Trans Pattern Anal Mach Intell 35(4):941–953CrossRefGoogle Scholar
  6. 6.
    Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2000, vol 2. IEEE, pp 142–149Google Scholar
  7. 7.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577CrossRefGoogle Scholar
  8. 8.
    Cota N, Kasetkasem T, Kovavisaruch L-O, Yamaoka K (2015) A robust moving object tracking. In: 2015 6th international conference of information and communication technology for embedded systems (IC-ICTES). IEEE, pp 1–6Google Scholar
  9. 9.
    Dinh TB, Vo N, Medioni G (2011) Context tracker: exploring supporters and distracters in unconstrained environments. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1177–1184Google Scholar
  10. 10.
    Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303–338CrossRefGoogle Scholar
  12. 12.
    Grabner H, Bischof H (2006) On-line boosting and vision. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 260–267Google Scholar
  13. 13.
    Grabner H, Grabner M, Bischof H (2006) Real-time tracking via on-line boosting. In: BMVC, vol 1, p 6Google Scholar
  14. 14.
    Guo K, Xu X, Qiu F, Chen J (2013) A novel incremental weighted pca algorithm for visual tracking. In: 2013 20th IEEE international conference on image processing (ICIP). IEEE, pp 3914–3918Google Scholar
  15. 15.
    Hale ET, Yin W, Zhang Y (2008) Fixed-point continuation for ∖ell_1-minimization: methodology and convergence. SIAM J Optim 19(3):1107–1130MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Hare S, Saffari A, Torr PH (2011) Struck: structured output tracking with kernels. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 263–270Google Scholar
  17. 17.
    Isard M, Blake A (1998) Condensation conditional density propagation for visual tracking. Int J Comput Vis 29(1):5–28CrossRefGoogle Scholar
  18. 18.
    Jepson AD, Fleet DJ, El-Maraghi TF (2003) Robust online appearance models for visual tracking. IEEE Trans Pattern Anal Mach Intell 25(10):1296–1311CrossRefGoogle Scholar
  19. 19.
    Jia X, Lu H, Yang M-H (2012) Visual tracking via adaptive structural local sparse appearance model. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1822–1829Google Scholar
  20. 20.
    Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: bootstrapping binary classifiers by structural constraints. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 49–56Google Scholar
  21. 21.
    Klein DA, Schulz D, Frintrop S, Cremers AB (2010) Adaptive real-time video-tracking for arbitrary objects. In: 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 772–777Google Scholar
  22. 22.
    Kwon J, Lee KM (2010) Visual tracking decomposition. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1269–1276Google Scholar
  23. 23.
    Mei X, Ling H (2009) Robust visual tracking using l1 minimization. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 1436–1443Google Scholar
  24. 24.
    Oron S, Bar-Hillel A, Levi D, Avidan S (2012) Locally orderless tracking. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1940–1947Google Scholar
  25. 25.
    Ross DA, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141CrossRefGoogle Scholar
  26. 26.
    Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. In: ACM transactions on graphics (TOG), vol 23. ACM, pp 309–314Google Scholar
  27. 27.
    Wang D, Lu H, Yang M-H (2013) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325MathSciNetCrossRefGoogle Scholar
  28. 28.
    Zha Y, Cao T, Huang H, Song Z, Liang W, Li F (2015) Robust object tracking via local constrained and online weighted. Multimedia Tools and Applications, pp 1–23Google Scholar
  29. 29.
    Zhang B, Li Z, Perina A, Del Bue A, Murino V (2015) Adaptive local movement modelling for object tracking. In: 2015 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 25–32Google Scholar
  30. 30.
    Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: Computer Vision–ECCV 2012. Springer, Berlin Heidelberg New York, pp 864–877CrossRefGoogle Scholar
  31. 31.
    Zhang K, Zhang L, Yang M-H, Zhang D Fast tracking via spatio-temporal context learning. arXiv:1311.1939
  32. 32.
    Zhou H, Yuan Y, Shi C (2009) Object tracking using sift features and mean shift. Comput Vis Image Underst 113(3):345–352CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Xiaofen Xing
    • 1
  • Fuhao Qiu
    • 1
  • Xiangmin Xu
    • 1
  • Chunmei Qing
    • 1
  • Yinrong Wu
    • 1
  1. 1.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations