ML Anthology

A Bayesian Non-Parametric Viewpoint to Visual Tracking

Yi Wang, Zhidong Li, Yang Wang, Fang Chen
WACV 2013 pp. 482-488
doi:10.1109/WACV.2013.6475058 /wacv/2013/wang2013wacv-bayesian/

Abstract

A novel bayesian non-parametric method for tracking is proposed in this paper. The foreground appearance distribution is modeled by unbounded mixtures controlled through a Bayesian non-parametric process. Two posterior inference strategies are provided: Gibbs sampling and sequential importance sampling. Both of these two sampling strategy benefits from the conjugate prior/posterior pairs by factorizing the joint posterior distributions. Once the mixture model is obtained/updated, the similarities/probablity of each observations assigned to this mixture model could be easily calculated. In model matching/verification, the Kullback-Leibler divergence and texture information is adopted for verification purpose. The robustness of our methods is demonstrated by the experiments.

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Cite

Text

Wang et al. "A Bayesian Non-Parametric Viewpoint to Visual Tracking." IEEE/CVF Winter Conference on Applications of Computer Vision, 2013. doi:10.1109/WACV.2013.6475058

Markdown

[Wang et al. "A Bayesian Non-Parametric Viewpoint to Visual Tracking." IEEE/CVF Winter Conference on Applications of Computer Vision, 2013.](https://mlanthology.org/wacv/2013/wang2013wacv-bayesian/) doi:10.1109/WACV.2013.6475058

BibTeX

@inproceedings{wang2013wacv-bayesian,
  title     = {{A Bayesian Non-Parametric Viewpoint to Visual Tracking}},
  author    = {Wang, Yi and Li, Zhidong and Wang, Yang and Chen, Fang},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2013},
  pages     = {482-488},
  doi       = {10.1109/WACV.2013.6475058},
  url       = {https://mlanthology.org/wacv/2013/wang2013wacv-bayesian/}
}