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Chinese Optics Letters

Chinese Optics Letters

| PUBLISHED MONTHLY BY CHINESE LASER PRESS AND DISTRIBUTED BY OSA

  • Vol. 4, Iss. 10 — Oct. 1, 2006
  • pp: 569–572

Joint tracking algorithm using particle filter and mean shift with target model updating

Bo Zhang, Weifeng Tian, and Zhihua Jin  »View Author Affiliations


Chinese Optics Letters, Vol. 4, Issue 10, pp. 569-572 (2006)


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Abstract

Roughly, visual tracking algorithms can be divided into two main classes: deterministic tracking and stochastic tracking. Mean shift and particle filter are their typical representatives, respectively. Recently, a hybrid tracker, seamlessly integrating the respective advantages of mean shift and particle filter (MSPF) has achieved impressive success in robust tracking. The pivot of MSPF is to sample fewer particles using particle filter and then those particles are shifted to their respective local maximum of target searching space by mean shift. MSPF not only can greatly reduce the number of particles that particle filter required, but can remedy the deficiency of mean shift. Unfortunately, due to its inherent principle, MSPF is restricted to those applications with little changes of the target model. To make MSPF more flexible and robust, an adaptive target model is extended to MSPF in this paper. Experimental results show that MSPF with target model updating can robustly track the target through the whole sequences regardless of the change of target model.

© 2006 Chinese Optics Letters

OCIS Codes
(100.0100) Image processing : Image processing
(100.2960) Image processing : Image analysis
(330.0330) Vision, color, and visual optics : Vision, color, and visual optics

Citation
Bo Zhang, Weifeng Tian, and Zhihua Jin, "Joint tracking algorithm using particle filter and mean shift with target model updating," Chin. Opt. Lett. 4, 569-572 (2006)
http://www.opticsinfobase.org/col/abstract.cfm?URI=col-4-10-569


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References

  1. D. Comaniciu, V. Ramesh, and P. Meer, IEEE Trans. Patt. Analy. and Mach. Intell. 25, 564 (2003).
  2. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, IEEE Trans. Signal Processing 50, 174 (2002).
  3. K. Nummiaro, E. Koller-Meier, and L. V. Gool, Image and Vision Computing 21, 99 (2003).
  4. C. Shan, Y. Wei, T. Tan, and F. Ojardias, in The Proceedings of 6th International Conference on Automatic Face and Gesture Recognition (2004).
  5. E. Maggio and A. Cavallaro, in Proceedings of IEEE Signal Processing Society International Conference on Acoustics, Speech, and Signal Processing Philadelphia USA (2005).
  6. S. Birchfield, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition California, USA (1998).
  7. M. Isard and A. Blake, Int. J. Computer Vision 29, 5 (1998).
  8. P. Li, T. Zhang, and A. E. C. Pece, Image and Vision Computing 21, 111 (2003).

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