Skip to main content
SpringerLink
Log in

Fusion tracking in color and infrared images using joint sparse representation

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

Abstract

Currently sparse signal reconstruction gains considerable interest and is applied in many fields. In this paper, a similarity induced by joint sparse representation is designed to construct the likelihood function of particle filter tracker so that the color visual spectrum and thermal spectrum images can be fused for object tracking. The proposed fusion scheme performs joint sparse representation calculation on both modalities and the resultant tracking results are fused using min operation on the sparse representation coefficients. In addition, a co-learning approach is proposed to update the reference templates of both modality and enhance the tracking robustness. The proposed fusion scheme outperforms state-of-the-art approaches, and its effectiveness is verified using OTCBVS database.

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. Conaire C O, O’Connor N E O, Cooke E, et al. Comparison of fusion methods for thermo-visual surveillance tracking. In: Proceedings of the 9th International Conference on Information Fusion, Florence, 2006. 1–7

  2. Walchshäusl L, Lindl R. Multi-sensor classification using a boosted cascade detector. In: Proceedings of the IEEE Intelligent Vehicles Symposium, Istanbul, 2007. 1045–1049

  3. Smith M, Heather J. A review of image fusion technology in 2005. In: Proceedings of SPIE, California, 2005. 29–45

  4. Cucchiara R. Multimedia surveillance systems. In: Proceedings of ACM International Workshop on Video Surveillance and Sensor Networks, New York, 2005. 3–10

  5. Hu W, Tan T, Wang L, et al. A survey on visual surveillance of object motion and behaviour. IEEE Trans Systems, Man Cybernetics, 2004, 34: 334–350

    Google Scholar 

  6. Davis J, Sharma V. Fusion-based background subtraction using contour saliency. In: Proceedings of IEEE International Workshop on Object Tracking and Classification Beyond the Visible Spectrum, San Diego, 2005. 1–8

  7. Bunyak F, Palaniappan K, Nath S K, et al. Geodesic active contour based fusion of visible and infrared video for persistent object tracking. In: Proceednigs of IEEE Workshop on Applications of Computer Vision (WACV), Austin, 2007. 35–42

  8. Conaire N O, O’Connor N E, Smeaton A. Thermo-visual feature fusion for object tracking using multiple spatiogram trackers. Mach Vis App, 2007, 7: 1–12

    Google Scholar 

  9. Doucet A, Freitas N, Gordon N. Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001

    MATH  Google Scholar 

  10. Isard M, Blake A. Condensation — conditional desity propagation for visual tracking. Int J Comput Vis, 1998, 29: 5–28

    Article  Google Scholar 

  11. Xue J, Zheng N, Zhong X. Sequential stratified sampling belief propagation for multiple targets tracking. Sci China Series F-Inf Sci, 2006, 49: 48–62

    Article  MathSciNet  MATH  Google Scholar 

  12. Perez P, Vermaak J, Blake A. Data fusion for visual tracking with particles. Proc IEEE, 2004, 92, 495–513

    Article  Google Scholar 

  13. Cvejic N, Nikolov S, Knowles H, et al. The effect of pixel-level fusion on object tracking in multi-sensor surveillance video. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), Minneapolis, 2007. 1–7

  14. Candes E, Tao T. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans Inf Theory, 2006, 52: 5406–5425

    Article  MathSciNet  Google Scholar 

  15. Dikmen M, Huang T. Robust estimation of foreground in surveillance videos by sparse error estimation. In: Proceedings of International Conference on Pattern Recognition (ICPR), Tampa, 2008. 1–4

  16. Mei X, Ling H. Robust visual tracking using l 1 minimization. In: Proceedings of International Conference on Computer Vision, Kyoto, 2009. 1–8

  17. Zhang J, Cai W, Tian Y, et al. Visual tracking via sparse representation based linear subspace model. In: Proceedings of International Conference on Computer and Information Technology, Xiamen, 2009. 166–171

  18. Liu H, Sun F. Visual tracking using sparsity induced similarity. In: Proceedings of International Conference on Pattern Recognition, Istanbul, 2010. 1702–1705

  19. Cheng H, Liu Z, Yang J. Sparsity induced similarity measure for label propagation. In: Proceedings of International Conference on Computer Vision (ICCV), Kyoto, 2009. 1–8

  20. Yan S, Wang H. Semi-supervised learning by sparse representation. In: Proceedings of SIAM on Data Mining, Sparks, 2009. 1–8

  21. Sun L, Liu J, Chen J, et al. Efficient recovery of jointly sparse vectors. In: Proceedings of Adavances in Neural Information Processing (NIPS), Vancouver, 2009. 1–9

  22. Chen J, Huo X. Theoretical results on sparse representations of multiple-measurement vectors. IEEE Trans Signal Process, 2006, 54: 4634–4643

    Article  Google Scholar 

  23. Yuan X, Yan S. Visual Classification with multi-task joint sparse representation In: Proceedings of Computer Vision and Pattern Recognition, Colorado Springs, 2011. 3493–3500

  24. Grabner H, Leistner C, Bischof H. Semi-supervised online boosting for robust tracking. In: Proceedings of European Conference on Computer Vision, Marseille, 2008. 234–247

  25. Liu R, Cheng J, Lu H. A robust boosting tracker with minimum error bound in a co-training framework. In: Proceedings International Conference on Computer Vision, Kyoto, 2009. 1459–1466

  26. Tang F, Brennan S, Zhao Q, et al. Co-tracking using semi-supervised support vector machines. In: Proceedings of International Conference on Computer Vision (ICCV), Rio De Janeiro, 2007. 1–8

  27. Yu Q, Dinh T, Medioni G. Online tracking and reacquisition using co-trained generative and discriminative trackers. In: Proceedings of European Conference on Computer Vision, Marseille, 2008. 1–14

  28. Ye W, Liu H, Sun F, et al. Vehicle tracking based on co-learning particle filter. In: Proceedings of Intelligent Robots and Systems (IROS), St. Louis, 2009. 2979–2984

  29. IEEE OTCBVS WS Series Bench. http://www.cse.ohio-state.edu/OTCBVS-BENCH

  30. Porikli F, Tuzel O, Meer P. Covariance tracking using model update based on Lie algebra. In: Proceedings of Computer Vision and Pattern Recogntion (CVPR), New York, 2006. 728–735

  31. Liu H, Sun F. Fusion tracking in color and infrared images using sequential belief propagation. In: Proceedings of International Conference on Robotics and Automation, Pasadena, 2008. 2259–2264

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    HuaPing Liu & FuChun Sun

  2. State Key Laboratory of Intelligent Technology and Systems, Beijing, 100084, China

    HuaPing Liu & FuChun Sun

  3. Tsinghua National Laboratory for Information Science and Technology, Beijing, 100084, China

    HuaPing Liu & FuChun Sun

Authors
  1. HuaPing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. FuChun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to FuChun Sun.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, H., Sun, F. Fusion tracking in color and infrared images using joint sparse representation. Sci. China Inf. Sci. 55, 590–599 (2012). https://doi.org/10.1007/s11432-011-4536-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-011-4536-9

Keywords

Search

Navigation