Skip to main content
SpringerLink
Log in

Dynamic multi-cue tracking using particle filter

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

We present a novel particle filter approach in this paper for robust object tracking using multi-cue association. In contrast to conventional particle trackers that are based on the single space, which does not provide enough information of good foreground/background discrimination, mapping the object into multiple spaces can help to seize the most informative properties of distinguishing the target from background. Moreover, the proposed algorithm commands all the spaces and their corresponding judgments in a global boosting view, which is implemented in an incremental manner to strengthen the ability of separating the target and background classes. The implicit appearance model is then used in updating step of the particle filter to measure the target region. Experimental results on several challenging video sequences have verified that the proposed method is compared very robust and effective with the traditional particle filter in many complicated scenes.

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

Similar content being viewed by others

References

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

  2. Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1830–1837 (2012)

  3. Cehovin, L., Kristan, M., Leonardis, A.: An adaptive coupled-layer visual model for robust visual tracking. In: IEEE International Conference on Computer Vision (2011)

  4. Collins, R.T., Yanxi, L., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005)

    Article  Google Scholar 

  5. Comaniciu, Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 234–240 (2003)

    Article  Google Scholar 

  6. Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 126–133 (2000)

  7. He, S., Yang, Q., Lau, R.W.H., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2427–2434 (2013)

  8. Isard, M., Blake, A.: Condensation: conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

    Article  Google Scholar 

  9. Jia, G., TianY., Wang, Y., Huang, T., Wang, M.: Dynamic multi-cue tracking with detection responses association. In: ACM conference on Multimedia, pp. 1171–1174 (2010)

  10. Kwolek, B.: Person following and mobile camera localization using particle filters. In: 4th International Workshop Robot Motion and Control, pp. 265–270 (2004)

  11. Kwon, J., Lee, K.M.: Visual tracking decomposition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269–1276 (2010)

  12. Kwon, J., Lee, K.M.: Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1208–1215 (2009)

  13. Moon, H., Chellappa, R.: 3d shape-encoded particle filter for object tracking and its application to human body tracking. EURASIP J. Image Video Process. 2008, Article ID 596989 (2008). doi:10.1155/2008/596986

  14. Nummiaro, K., Koller-Meier, E., Gool, L.V.: An adaptive color-based particle filter. Image Vision Comput. 21(1), 99–110 (2003)

    Article  Google Scholar 

  15. Perez, P., Vermaak, J., Blake, A.: Data fusion for visual tracking with particles. In: Proceedings of the Institute of Electronics Engineers, pp. 495–513 (2004)

  16. Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: IEEE Conference on European Conference on Computer Vision, pp. 661–675 (2002)

  17. Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1910–1917 (2012)

  18. Shahed, S.M.N., Ho, J., Yang, M.H.: Visual tracking with histograms and articulating blocks. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)

  19. Sullivan, J., Blake, A., Isard, M., MacCormick, J.: Object localization by Bayesian correlation. In: IEEE Conference on ICCV, Kerkyra, Greece, pp. 1068–1075 (1999)

  20. Sun, X., Zhang, J., Xie, Z., et al.: Active-matting-based object tracking with color cues. Signal Image Video Process. (2014). doi:10.1007/s11760-014-0637-4

  21. Wang, X., Wang, Y., Wan, W., Hwang, J.: Object tracking with sparse representation and annealed particle filter. Signal Image Video Process. (2014). doi:10.1007/s11760-014-0628-5

  22. Zhang, L., van der Maaten, L.: Structure preserving object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1838–1845 (2013)

  23. Zhong, B., Yao, H., Chen, S., Ji, R., Yuan, X., Liu, S., Gao, W.: Visual tracking via weakly supervised learning from multiple imperfect oracles. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1323–1330 (2010)

Download references

Author information

Authors and Affiliations

  1. Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China

    Xin Sun, Hongxun Yao & Xiusheng Lu

Authors
  1. Xin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongxun Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiusheng Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, X., Yao, H. & Lu, X. Dynamic multi-cue tracking using particle filter. SIViP 8 (Suppl 1), 95–101 (2014). https://doi.org/10.1007/s11760-014-0674-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-014-0674-z

Keywords

Search

Navigation