Skip to main content
SpringerLink
Log in

Target Tracking Based on Biological-Like Vision Identity via Improved Sparse Representation and Particle Filtering

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

To effectively track targets under partial occlusion and illumination variation, an improved target tracking method based on combination of sparse representation and particle filtering is proposed in this paper. We regard the candidate target particle set as redundant dictionary and the target template as observation signal to reduce the computational complexity and enhance the real-time performance of target tracking. Besides, to enhance tracking robustness for better adaption to illumination and occlusion, the density histogram, local binary pattern feature fusion, trivial templates and energy control parameters are also utilized in this study. Finally, extensive simulation experiments under different circumstances show that the proposed method performs better compared with other methods, and the average computation time decreases greatly.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Mei X, Ling H. Robust visual tracking using L1 minimization. In: Proceedings of the 12th international conference on computer vision. 2009. p. 1436–43.

  2. Lim J, Ross DA, Lin RS, et al. Incremental learning for visual tracking. In: Advances in neural information processing system (NIPS 2004), vol. 17, 13-18 December 2004, Vancouver, British Columbia. DBLP; 2004.

  3. Zhong W, Lu HC, Yang MH. Robust object tracking via sparsity-based collaborative model. In: Proceedings of the 25th IEEE international conference on computer vision and pattern recognition (CVPR), Providence. 2012. p. 1838–45.

  4. Chenouard N, Smal I, De Chaumont F, et al. Objective comparison of particle tracking methods. Nat Methods. 2014;11(3):281–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bouaynaya N, Schonfeld D. On the optimality of motion-based particle filtering. IEEE Trans Circuits Syst Video Technol. 2009;19(7):1068–72.

    Article  Google Scholar 

  6. Chen C, Schonfeld D. A particle filtering framework for joint video tracking and pose estimation. IEEE Trans Image Process. 2010;19(6):1625–34.

    Article  PubMed  Google Scholar 

  7. Kwon J, Lee KM. Visual tracking decomposition. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), IEEE. 2010. p. 1269–76.

  8. Ross DA, Lim J, Lin RS, et al. Incremental learning for robust visual tracking. Int J Comput Vis. 2008;77(1–3):125–41.

    Article  Google Scholar 

  9. Isard M, Blake A. ICONDENSATION: unifying low-level and high-level tracking in a stochastic framework. In: Computer vision ECCV98. Berlin: Springer. 1998. p. 893–908.

  10. Shen C, Van den Hengel A, Dick A. Probabilistic multiple cue integration for particle filter based tracking. In: Proceedings of the 7th digital image computing: techniques and applications. 2003. p. 399–408.

  11. Wright J, Yang AY, Ganesh A, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell. 2009;31(2):210–27.

    Article  PubMed  Google Scholar 

  12. Cevher V, Sankaranarayanan A, Duarte MF, et al. Compressive sensing for background subtraction. In: Computer vision—ECCV 2008. Berlin: Springer. 2008. p. 155–68.

  13. Gu J, Nayar SK, Grinspun E, et al. Compressive structured light for recovering inhomogeneous participating media. IEEE Trans Pattern Anal Mach Intell. 2013;35(3):555–67.

    PubMed  Google Scholar 

  14. Mairal J, Bach F, Ponce J, et al. Discriminative learned dictionaries for local image analysis. In: IEEE conference on computer vision and pattern recognition, 2008, CVPR 2008. IEEE. 2008. p. 1–8.

  15. Mei X, Ling H. Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell. 2011;33(11):2259–72.

    Article  PubMed  Google Scholar 

  16. Bao C, Wu Y, Ling H, et al. Real time robust L1 tracker using accelerated proximal gradient approach. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE. 2012. p. 1830–7.

  17. Zhang Z, Xu H, Chao Z, et al. A novel vehicle reversing speed control based on obstacle detection and sparse representation. IEEE Trans Intell Transp Syst. 2015;16(3):1321–34.

    Article  Google Scholar 

  18. Zhang S, Yao H, Sun X, et al. Target tracking based on sparse coding. Intell Comput Appl. 2013;3(1):21–5.

    Google Scholar 

  19. Mei X, Ling H, Wu Y, et al. Minimum error bounded efficient L1 tracker with occlusion detection. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE. 2011. p. 1257–64.

  20. Zhang K, Zhang L, Yang MH, et al. Fast tracking via spatiotemporal context learning. arXiv preprint arXiv:1311.1939. 2013.

Download references

Acknowledgments

This study was funded by the National Natural Science Foundation of People’s Republic of China (Grant No. 91026005). We wish to thank Dr. Zhang Shuang who has contributed to the paper improvement.

Author information

Authors and Affiliations

  1. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Gun Li & Zhong-yuan Liu

  2. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Hou-biao Li

  3. College of Information and Control Engineering, China University of Petroleum, Qingdao, 266580, China

    Peng Ren

Authors
  1. Gun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong-yuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hou-biao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gun Li.

Ethics declarations

Conflict of Interest

Gun Li, Zhong-yuan Liu, Hou-biao Li, and Peng Ren declare that they have no conflict of interest.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

Human and Animal Rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, G., Liu, Zy., Li, Hb. et al. Target Tracking Based on Biological-Like Vision Identity via Improved Sparse Representation and Particle Filtering. Cogn Comput 8, 910–923 (2016). https://doi.org/10.1007/s12559-016-9410-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-016-9410-z

Keywords

Search

Navigation