Skip to main content

Robust Motion Tracking in Video Sequences Using Particle Filter

  • Conference paper
Advances in Artificial Reality and Tele-Existence (ICAT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4282))

Included in the following conference series:

  • 2272 Accesses

Abstract

A robust motion tracking algorithm based on color and motion information was presented. Color is an effective feature in visual object tracking because of its robustness against rotation and scale variation. Nevertheless, the color of an object may change with varying illuminations, different image capture devices and different visual positions. Here, the color and motion information were fused in our visual tracking applications. Particle filter was employed as the essential framework because of its capacity of dealing with Non-linear/Non-Gaussian models by randomly sampling in state space. A particle filter can generate several hypotheses simultaneously in state space by randomly sampling and evaluate the states by weighing them respectively. The similarity between prediction data and observation information depends on the integration of Bhattacharyya distance and spacial Euclidean distance. Experimental results show the effectiveness of the proposed approach.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. Radar and Signal Processing 140(2), 107–113 (1993)

    Article  Google Scholar 

  2. Isard, M., Blake, A.: Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)

    Article  Google Scholar 

  3. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: CVPR 2000, pp. 142–149 (2000)

    Google Scholar 

  4. Nummiaro, K., Koller-Meier, E., Van Gool, L.: An Adaptive Color-Based Particle Filter. Journal of Image and Vision Computing 21(1), 99–110 (2003)

    Article  MATH  Google Scholar 

  5. Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  6. Wu, Y., Huang, T.S.: A co-inference approach to robust visual tracking. In: Proc. Int’l. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 26–33 (2001)

    Google Scholar 

  7. http://www.research.ibm.com/peoplevision/performanceevaluation.html

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, G., Fan, C., Gao, E. (2006). Robust Motion Tracking in Video Sequences Using Particle Filter. In: Pan, Z., Cheok, A., Haller, M., Lau, R.W.H., Saito, H., Liang, R. (eds) Advances in Artificial Reality and Tele-Existence. ICAT 2006. Lecture Notes in Computer Science, vol 4282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941354_55

Download citation

  • DOI: https://doi.org/10.1007/11941354_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49776-9

  • Online ISBN: 978-3-540-49779-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics