conference paper · British Machine Vision Conference (BMVC) · 2006

Graphical Model based Cue Integration Strategy for Head Tracking

Cited by 13Abstract and details only

Abstract

To achieve robust system, more and more vision researchers take into account fusing multiple visual cues. In this paper, we propose a novel strategy to integrate multiple naive cues for head tracking. Firstly, a cue dependency model is constructed via graphical model. Secondly, a new inference procedure based on non-parametric belief propagation is built for cue integration. The work presented is thus a general framework easy to extend for other computer vision research problems. Experimental results imply that the strategy we propose is effective, and it is robust without estimation of cue reliability. 1

Cite this paper

APA 7

Zhong, X., Xue, J., & Zheng, N. (2006). Graphical Model based Cue Integration Strategy for Head Tracking. British Machine Vision Conference (BMVC), 22.1–22.10. https://doi.org/10.5244/c.20.22

MLA 9

Zhong, Xian, et al. “Graphical Model based Cue Integration Strategy for Head Tracking.” British Machine Vision Conference (BMVC), 2006, pp. 22.1–22.10, https://doi.org/10.5244/c.20.22.

Chicago

Zhong, Xian, Jianru Xue, and Nanning Zheng. 2006. “Graphical Model based Cue Integration Strategy for Head Tracking.” British Machine Vision Conference (BMVC): 22.1–22.10. https://doi.org/10.5244/c.20.22.

Harvard

Zhong, X., Xue, J. and Zheng, N. (2006) ‘Graphical Model based Cue Integration Strategy for Head Tracking’, British Machine Vision Conference (BMVC), pp. 22.1–22.10. Available at: https://doi.org/10.5244/c.20.22.

BibTeX

@article{zhong2006graphical,
  title = {{Graphical Model based Cue Integration Strategy for Head Tracking}},
  author = {Zhong, Xian and Xue, Jianru and Zheng, Nanning},
  journal = {British Machine Vision Conference (BMVC)},
  pages = {22.1--22.10},
  year = {2006},
  doi = {10.5244/c.20.22}
}

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