Multimedia Tools and Applications

, Volume 78, Issue 3, pp 3689–3703 | Cite as

Robust object tracking via constrained online dictionary learning

  • Na LiuEmail author
  • Hong Huo
  • Tao Fang


Robust object tracking has widespread applications in human motion analysis systems, but it is challenging due to various factors, such as occlusion, illumination variation, and complex backgrounds. In this paper, we present a novel tracking method on the basis of a constrained online dictionary learning algorithm. Some existing tracking methods cannot consider background effects and thus have weak discriminative ability. Moreover, some dictionary learning-based tracking methods directly collect target templates and background templates as positive and negative dictionaries, respectively. The main issue is that the dictionaries cannot effectively represent the target and background and handle appearance changes. Thus, a constrained online dictionary learning algorithm is proposed to obtain a discriminative dictionary, which can ensure that the proposed tracker has good discriminative ability in distinguishing targets from complex backgrounds. Experimental results show that the proposed algorithm performs favorably against other state-of-the-art methods in terms of accuracy and robustness.


Object tracking Human motion analysis Dictionary learning Appearance changes 


  1. 1.
    Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: 2006 IEEE Computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 798–805Google Scholar
  2. 2.
    Aharon M, Elad M, Bruckstein A (2006) k-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54 (11):4311–4322CrossRefGoogle Scholar
  3. 3.
    Babenko B, Yang M-H, Belongie S (2009) Visual tracking with online multiple instance learning. In: CVPR. IEEE, pp 983–990Google Scholar
  4. 4.
    Bao C, Wu Y, Ling H, Ji H (2012) Real time robust L1 tracker using accelerated proximal gradient approach. In: CVPR. IEEE, pp 1830–1837Google Scholar
  5. 5.
    Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems, pp 585–591Google Scholar
  6. 6.
    Cheng X, Zhang Y, Cui J, Zhou L (2017) Object tracking via temporal consistency dictionary learning. IEEE Trans Syst Man Cybern Syst 47(4):628–638CrossRefGoogle Scholar
  7. 7.
    Dou J, Qin Q, Tu Z (2017) Robust visual tracking based on generative and discriminative model collaboration. Multimed Tools Appl 76(14):15839–15866CrossRefGoogle Scholar
  8. 8.
    Hoseinnezhad R, Vo BN, Vo BT (2013) Visual tracking in background subtracted image sequences via multi-Bernoulli filtering. IEEE Trans Signal Process 61 (2):392–397MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: bootstrapping binary classifiers by structural constraints. In: CVPR. IEEE, pp 49–56Google Scholar
  10. 10.
    Kwon J, Lee K-M (2010) Visual tracking decomposition. In: CVPR. IEEE, pp 1269–1276Google Scholar
  11. 11.
    Li H, Shen C, Shi Q (2011) Real-time visual tracking using compressive sensing. In: 2011 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 1305–1312Google Scholar
  12. 12.
    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
  13. 13.
    Oron S, Bar-Hillel A, Levi D, Avidan S (2015) Locally orderless tracking. Int J Comput Vis 111(2):213–228MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ou W, Yuan D, Liu Q et al. (2017) Object tracking based on online representative sample selection via non-negative least square. Multimed Tools Appl 1–19Google Scholar
  15. 15.
    Ross D, Lim J, Lin R, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1):125–141CrossRefGoogle Scholar
  16. 16.
    Taalimi A, Qi H, Khorsandi R Online multi-modal task-driven dictionary learning and robust joint sparse representation for visual tracking. In: 2015 12th IEEE International conference on advanced video and signal based surveillance (AVSS). IEEE, pp 1–6Google Scholar
  17. 17.
    Tang S, Zhang LF, Yan JL, Tan XW, Ding GY (2016) An online LC-KSVD based dictionary learning for multi-target tracking. In: 2016 International conference on information system and artificial intelligence (ISAI). IEEE, pp 630–633Google Scholar
  18. 18.
    Tropp J, Gilbert A (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666MathSciNetCrossRefGoogle Scholar
  19. 19.
    Wang S, Fu Y (2015) Locality-constrained discriminative learning and coding. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 17–24Google Scholar
  20. 20.
    Wang D, Lu H, Yang MH (2013) Least soft-threshold squares tracking[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2371–2378Google Scholar
  21. 21.
    Wang N, Wang J, Yeung DY (2013) Online robust non-negative dictionary learning for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 657–664Google Scholar
  22. 22.
    Wang D, Sun W, Yu S et al. (2016) A novel background-weighted histogram scheme based on foreground saliency for mean-shift tracking. Multimed Tools Appl 75 (17):10271–10289CrossRefGoogle Scholar
  23. 23.
    Xie Y, Zhang W, Li C, Lin S, Qu Y, Zhang Y (2014) Discriminative object tracking via sparse representation and online dictionary learning. IEEE Trans Cybern 44(4):539–553CrossRefGoogle Scholar
  24. 24.
    Xie X, Jones M, Tam G (2017) Recognition, tracking, and optimisation[J]. Int J Comput Vis 122(3):409–410CrossRefGoogle Scholar
  25. 25.
    Xing J, Gao J, Li B, Hu W, Yan S (2013) Robust object tracking with online multi-lifespan dictionary learning. In: Proceedings of the IEEE International conference on computer vision, pp 665–672Google Scholar
  26. 26.
    Yang M, Zhang L, Yang J, Zhang D (2010) Metaface learning for sparse representation based face recognition. In: 2010 17th IEEE International conference on image processing (ICIP). IEEE, pp 1601–1604Google Scholar
  27. 27.
    Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn 46(1):397–411MathSciNetCrossRefGoogle Scholar
  28. 28.
    Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: ECCV. Springer, pp 864–877Google Scholar
  29. 29.
    Zhang T, Ghanem B, Liu S, Ahuja N (2012) Robust visual tracking via multi-task sparse learning. In: CVPR. IEEE, pp 2042–2049Google Scholar
  30. 30.
    Zhang S, Yao H, Sun X, Lu X (2013) Sparse coding based visual tracking: review and experimental comparison. Pattern Recogn 46(7):1772–1788CrossRefGoogle Scholar
  31. 31.
    Zhang T, Ghanem B, Liu S, Ahuja N (2013) Robust visual tracking via structured multi-task sparse learning. Int J Comput Vis 101(2):367–383MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of AutomationShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations