Skip to main content
SpringerLink
Log in

Visual tracking using discriminative representation with ℓ2 regularization

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

In this paper, we propose a novel visual tracking method using a discriminative representation under a Bayesian framework. First, we exploit the histogram of gradient (HOG) to generate the texture features of the target templates and candidates. Second, we introduce a novel discriminative representation and ℓ2-regularized least squares method to solve the proposed representation model. The proposed model has a closed-form solution and very high computational efficiency. Third, a novel likelihood function and an update scheme considering the occlusion factor are adopted to improve the tracking performance of our proposed method. Both qualitative and quantitative evaluations on 15 challenging video sequences demonstrate that our method can achieve more robust tracking results in terms of the overlap rate and center location error.

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. Li A, Lin M, Wu Y, Yang M H, Yan S C. NUS-PRO: a new visual tracking challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 335–349

    Article  Google Scholar 

  2. Wu Y, Lim J, Yang M H. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834–1848

    Article  Google Scholar 

  3. Zhang K H, Zhang L, Yang M H. Fast compressive tracking. IEEE Transations on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002–2015

    Article  Google Scholar 

  4. Li X, Shen C H, Dick A, Hengel A. Learning compact binary codes for visual tracking. In: Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2419–2426

    Google Scholar 

  5. Zhang K H, Zhang L, Yang M H, Hu Q H. Robust object tracking via active feature selection. IEEE Transactions Circuits and Systems for Video Technology, 2013, 23(11): 1957–1967

    Article  Google Scholar 

  6. Song H H. Robust visual tracking via online informative feature selection. Electronics Letters, 2014, 50(25): 1931–1933.

    Article  Google Scholar 

  7. Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning. In: Proceedings of the 22nd IEEE Conference on Computer Vision and Pattern Recognition. 2009, 983–990

    Google Scholar 

  8. Zhang K H, Liu Q S, Wu Y, Yang M H. Robust visual tracking via convolutional networks without training. IEEE Transations on Image Processing, 2016, 25(4): 1779–1792

    MathSciNet  Google Scholar 

  9. Yan J, Chen X, Deng D X, Zhu Q P. Visual object tracking via online sparse instance learning. Journal of Visual Communication and Image Representation, 2015, 26: 231–246

    Article  Google Scholar 

  10. Zhang K H, Zhang L, Yang M H. Real-time object tracking via online discriminative feature selection. IEEE Transactions on Image Processing, 2013, 22(12): 4664–4677

    Article  MathSciNet  MATH  Google Scholar 

  11. Song H H, Zheng Y H, Zhang K H. Robust visual tracking via self-similarity learning. Electronics Letters, 2017, 53(1): 20–22

    Article  Google Scholar 

  12. Yang X, Wang M, Zhang L M, Sun F M, Hong R C, Qi M B. An efficient tracking system by orthogonalized templates. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3187–3197

    Article  Google Scholar 

  13. Wang D, Lu H C, Xiao Z Y, Yang M H. Inverse sparse tracker with a locally weighted distance metric. IEEE Transactions on Image Processing, 2015, 24(9): 2646–2657

    Article  MathSciNet  Google Scholar 

  14. Wang D, Lu H C. Online visual tracking via two view sparse representation. IEEE Signal Processing Letters, 2014, 21(9): 1031–1034

    Article  Google Scholar 

  15. Han Y H, Yang Y, Yan Y, Ma Z G, Sebe N, Zhou X F. Semisupervised feature selection via spline regression for video semantic recognition. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(2): 252–264

    Article  MathSciNet  Google Scholar 

  16. Han Y H, Wu F, Tian Q, Zhuang Y T. Image annotation by input-output structural grouping sparsity. IEEE Transactions on Image Processing, 2012, 21(6): 3066–3079

    Article  MathSciNet  MATH  Google Scholar 

  17. Yang J, Chu D L, Zhang L, Xu Y, Yang J Y. Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(7): 1023–1035

    Article  Google Scholar 

  18. Wright J, Yang A Y, Ganesh A, Sastry S, Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210–227

    Article  Google Scholar 

  19. Zhuang B H, Lu H C, Xiao Z Y, Wang D. Visual tracking via discriminative sparse similarity map. IEEE Transactions on Image Processing, 2014, 23(4): 1872–1881

    Article  MathSciNet  MATH  Google Scholar 

  20. Hu HW, Ma B, Jia Y D. Multi-task L0 gradient minimization for visual tracking. Neurocomputing, 2015, 154(22): 41–49

    Google Scholar 

  21. Yoon J H, Yang M H, Yoon K J. Interacting multiview tracker. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(5): 903–917

    Article  Google Scholar 

  22. Pan J S, Lim J, Su Z X, Yang M H. L0-regularized object representation for visual tracking. In: Proceedings of the British Machine Vision Conference. 2014, 1–12

    Google Scholar 

  23. Ma B, Shen J B, Liu Y B, Hu H W, Shao L, Li X L. Visual tracking using strong classifier and structural local sparse descriptors. IEEE Transactions on Multimedia, 2015, 17(10): 1818–1828

    Article  Google Scholar 

  24. Mei X, Ling H B. Robust visual tracking using 1 minimization. In: Proceedings of the 12th IEEE International Conference on Computer Vision. 2009, 1436–1443

    Google Scholar 

  25. Bao C L, Wu Y, Ling H B, Ji H. Real time robust 1 tracker using accelerated proximal gradient approach. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1830–1837

    Google Scholar 

  26. Jia X, Lu H C, Yang M H. Visual tracking via coarse and fine structural local sparse appearance models. IEEE Transactions on Image Processing, 2016, 25(10): 4555–4564

    Article  MathSciNet  Google Scholar 

  27. Zhong W, Lu H C, Yang M H. Robust object tracking via sparse collaborative appearance model. IEEE Transactions on Image Processing, 2014, 23(5): 2356–2368

    Article  MathSciNet  MATH  Google Scholar 

  28. Wang D, Lu H C, Yang M H. Least soft-threshold squares tracking. In: Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2371–2378

    Google Scholar 

  29. Wu Y W, Yuan J S, Tan P Y, Jia Y D, Zhang J. Robust distracter-resistive tracker via learning a multi-component discriminative dictionary. IEEE Transactions on Image Processing, submitted.

  30. Wang D, Lu H C, Yang M H. Kernel collaborative face recognition. Pattern Recognition, 2015, 48(10): 3025–3237

    Article  Google Scholar 

  31. Zhang L, Yang M H, Feng X C. Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of the 13th IEEE International Conference on Computer Vision. 2011, 471–478

    Google Scholar 

  32. Cai S J, Zhang L, Zuo W M, Feng X C. A probabilistic collaborative representation based approach for pattern classification. In: Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. 2016, 2950–2959

    Google Scholar 

  33. Shi S F, Eriksson A, Hengel A, Shen C H. Is face recognition really a compressive sensing problem? In: Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition. 2011, 553–560

    Google Scholar 

  34. Xiao Z Y, Lu H C, Wang D. L2-RLS based object tracking. IEEE Transaction on Circuits and Systems for Video Technology, 2014, 24(8): 1301–1308

    Article  Google Scholar 

  35. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the 18th IEEE Conference on Computer Vision and Pattern Recognition. 2005, 886–893

    Google Scholar 

  36. Henriques J, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European Conference on Computer Vision. 2012, 702–715

    Google Scholar 

  37. Xu Y, Zhong Z F, Yang J, You J, Zhang D. A new discriminative sparse representation method for robust face recognition via 2 regularization. IEEE Transactions on Neural Networks and Learning Systems, 2016, PP(99): 1–10

    Google Scholar 

  38. Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829

    Google Scholar 

  39. Wang D, Lu H C. On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization. Signal Processing, 2013, 93(6): 1608–1623

    Article  Google Scholar 

  40. Wang D, Lu H C, Yang M H. Online object tracking with sparse prototypes. IEEE Transactions on Image Processing, 2013, 22(1): 314–325

    Article  MathSciNet  MATH  Google Scholar 

  41. Wang D, Lu H C. Visual tracking via probability continuous outlier model. In: Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition. 2014, 3478–3485

    Google Scholar 

  42. Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In: Proceedings of the 19th IEEE Conference on Computer Vision and Pattern Recognition. 2006, 798–805

    Google Scholar 

  43. Kwon J S, Lee K M. Visual tracking decomposition. In: Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1269–1276

    Google Scholar 

Download references

Acknowledgements

This work was supported by a project of Shandong Province Higher Educational Science and Technology Program (J17KA088 and J16LN02), the Natural Science Foundation of Shandong Province (ZR2015FL009 and ZR2014FL020), the Key Research and Development Program of Shandong Province (2016GGX101023), and the Science Foundation of Binzhou University (BZXYG1524).

Author information

Authors and Affiliations

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Haijun Wang & Hongjuan Ge

  2. Aviation Information Technology Research and Development Center, Binzhou University, Binzhou, 256603, China

    Haijun Wang

Authors
  1. Haijun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongjuan Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haijun Wang.

Additional information

Haijun Wang received his MS degree in School of Information Science and Engineering from Shandong University, China in 2007. He is currently a PhD candidate in the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, China. He is also with the Flying College of Bin Zhou University, China. His research interests include visual tracking and image segmentation.

Hongjuan Ge received her PhD degree in electric machine and electric appliance from Nanjing University of Aeronautics and Astronautics, China in 2007. She is a professor in the College of Civil Aviation in Nanjing University of Aeronautics and Astronautics, China. Her current research interests include electric machine appliance and airplane equipment design research.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Ge, H. Visual tracking using discriminative representation with ℓ2 regularization. Front. Comput. Sci. 13, 199–211 (2019). https://doi.org/10.1007/s11704-017-6434-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-017-6434-9

Keywords

Search

Navigation