ML Anthology

Bayesian Human Segmentation in Crowded Situations

Tao Zhao, Ramakant Nevatia
CVPR 2003 pp. 459-466
doi:10.1109/CVPR.2003.1211503 /cvpr/2003/zhao2003cvpr-bayesian/

Abstract

The problem of segmenting individual humans in crowded situations from stationary video camera sequences is exacerbated by object inter-occlusion. We pose this problem as a "model-based segmentation" problem in which human shape models are used to interpret the foreground in a Bayesian framework. The solution is obtained by using an efficient Markov chain Monte Carlo (MCMC) method that uses domain knowledge as proposal probabilities. Knowledge of various aspects including human shape, human height, camera model, and image cues including human head candidates, foreground/background separation are integrated in one theoretically sound framework. We show promising results and evaluations on some challenging data.

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Cite

Text

Zhao and Nevatia. "Bayesian Human Segmentation in Crowded Situations." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003. doi:10.1109/CVPR.2003.1211503

Markdown

[Zhao and Nevatia. "Bayesian Human Segmentation in Crowded Situations." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003.](https://mlanthology.org/cvpr/2003/zhao2003cvpr-bayesian/) doi:10.1109/CVPR.2003.1211503

BibTeX

@inproceedings{zhao2003cvpr-bayesian,
  title     = {{Bayesian Human Segmentation in Crowded Situations}},
  author    = {Zhao, Tao and Nevatia, Ramakant},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2003},
  pages     = {459-466},
  doi       = {10.1109/CVPR.2003.1211503},
  url       = {https://mlanthology.org/cvpr/2003/zhao2003cvpr-bayesian/}
}