Skip to main content
SpringerLink
Log in

Learning online structural appearance model for robust object tracking

基于在线学习结构化表观模型的视觉目标跟踪方法

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

The main challenge of robust object tracking comes from the difficulty in designing an adaptive appearance model that is able to accommodate appearance variations. Existing tracking algorithms often perform self-updating of the appearance model with examples from recent tracking results to account for appearance changes. However, slight inaccuracy of tracking results can degrade the appearance model. In this paper, we propose a robust tracking method by evaluating an online structural appearance model based on local sparse coding and online metric learning. Our appearance model employs pooling of structural features over the local sparse codes of an object region to obtain a middle-level object representation. Tracking is then formulated by seeking for the most similar candidate within a Bayesian inference framework where the distance metric for similarity measurement is learned in an online manner to match the varying object appearance. Both qualitative and quantitative evaluations on various challenging image sequences demonstrate that the proposed algorithm outperforms the state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, 2006. 798–805

    Google Scholar 

  2. Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell, 2003, 25: 564–577

    Article  Google Scholar 

  3. Kwon J, Lee K M. Visual tracking decomposition. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 1269–1276

    Google Scholar 

  4. Wang H Z, Suter D, Schindler K, et al. Adaptive object tracking based on an effective appearance filter. IEEE Trans Pattern Anal Mach Intel, 2007, 29: 1661–1667

    Article  Google Scholar 

  5. Grabner H, Grabner M, Bischof H. Real-time tracking via on-line boosting. In: Proceedings of British Machine Vision Conference, Edinburgh, 2006. 6–15

    Google Scholar 

  6. Babenko B, Yang M H, Belongie S. Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intel, 2011, 33: 1619–1632

    Article  Google Scholar 

  7. Zhang K H, Zhang L, Yang M H. Real-time compressive tracking. In: Proceedings of European Conference on Computer Vision, Berlin Heidelberg, 2012. 864–877

    Google Scholar 

  8. Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intel, 2012, 34: 1409–1422

    Article  Google Scholar 

  9. Li X, Shen C H, Dick A, et al. Learning compact binary codes for visual tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, 2013. 2419–2426

    Google Scholar 

  10. Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking. Int J Comput Vis, 2008, 77: 125–141

    Article  Google Scholar 

  11. Wang D, Lu H C, Yang M H. Online object tracking with sparse prototypes. IEEE Trans Image Process, 2013, 22: 314–325

    Article  MathSciNet  Google Scholar 

  12. Wang D, Lu H C, Yang M H. Least soft-threshold squares tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, 2013. 2371–2378

    Google Scholar 

  13. Li X, Dick A, Shen C H, et al. Incremental learning of 3D-DCT compact representations for robust visual tracking. IEEE Trans Pattern Anal Mach Intel, 2013, 35: 863–881

    Article  Google Scholar 

  14. Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intel, 2009, 31: 210–227

    Article  Google Scholar 

  15. Mei X, Ling H B. Robust visual tracking using ℓ1 minimization. In: Proceedings of IEEE International Conference on Computer Vision, Kyoto, 2009. 1436–1443

    Google Scholar 

  16. Li H X, Shen C H, Shi Q F. Real-time visual tracking using compressive sensing. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs, 2011. 1305–1312

    Google Scholar 

  17. Liu B Y, Huang J Z, Yang L, et al. Robust tracking using local sparse appearance model and k-selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, 2011. 1313–1320

    Google Scholar 

  18. Bao C, Wu Y, Ling H, et al. Real time robust ℓ1 tracker using accelerated proximal gradient approach. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, 2012. 1830–1837

    Google Scholar 

  19. Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, 2012. 1822–1829

    Google Scholar 

  20. Zhang T Z, Ghanem B, Liu S, et al. Robust visual tracking via multi-task sparse learning. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, 2012. 2042–2049

    Google Scholar 

  21. Zhong W, Lu H C, Yang M H. Robust object tracking via sparsity-based collaborative model. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, 2012. 1838–1845

    Google Scholar 

  22. Wang Q, Chen F, Yang J M, et al. Transferring visual prior for online object tracking. IEEE Trans Image Process, 2012, 21: 3296–3305

    Article  MathSciNet  Google Scholar 

  23. Wang J J, Yang J C, Yu K, et al. Locality-constrained linear coding for image classification. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 3360–3367

    Google Scholar 

  24. Yang J C, Yu K, Gong Y H, et al. Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, 2009. 1794–1801

    Google Scholar 

  25. Li X, Hu W M, Shen C H, et al. A survey of appearance models in visual object tracking. ACM Trans Intell Sys Technol, 2013, 4: 58

    Google Scholar 

  26. Jiang N, Liu W, Wu Y. Learning adaptive metric for robust visual tracking. IEEE Trans Image Process, 2011, 20: 2288–2300

    Article  MathSciNet  Google Scholar 

  27. Wang X Y, Hua G, Han T X. Discriminative tracking by metric learning. In: Proceedings of European Conference on Computer Vision, Heraklion, 2010. 200–214

    Google Scholar 

  28. Wu Y, Ma B. Learning distance metric for object contour tracking. Pattern Anal Appl, 2012, 1–13

    Google Scholar 

  29. Li X, Shen C H, Shi Q F, et al. Non-sparse linear representations for visual tracking with online reservoir metric learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2012. 1760–1767

    Google Scholar 

  30. Tsagkatakis G, Savakis A. Online distance metric learning for object tracking. IEEE Trans Circuit Syst Video Technol, 2011, 21: 1810–1821

    Article  Google Scholar 

  31. Dasgupta S, Gupta A. An elementary proof of a theorem of Johnson and Lindenstrauss. Random Struct Algorithms, 2003, 22: 60–65

    Article  MATH  MathSciNet  Google Scholar 

  32. Li P, Hastie T J, Church K W. Very sparse random projections. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, 2006. 287–296

    Chapter  Google Scholar 

  33. Shalev-Shwartz S, Singer Y, Ng A Y. Online and batch learning of pseudo-metrics. In: Proceedings of ACM International Conference on Machine Learning, Banff, 2004. 94–102

    Google Scholar 

  34. Isard M, Blake A. Condensation-conditional density propagation for visual tracking. Int J Comput Vis, 1998, 29: 5–28

    Article  Google Scholar 

  35. Liu L, Fieguth P. Texture classification from random features. IEEE Trans Pattern Anal Mach Intel, 2012, 34: 574–586

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China

    Min Yang, MingTao Pei, YuWei Wu & YunDe Jia

Authors
  1. Min Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. MingTao Pei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YuWei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. YunDe Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to MingTao Pei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, M., Pei, M., Wu, Y. et al. Learning online structural appearance model for robust object tracking. Sci. China Inf. Sci. 58, 1–14 (2015). https://doi.org/10.1007/s11432-014-5177-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-014-5177-6

Keywords

关键词

Search

Navigation