Bayesian Human Segmentation in Crowded Situations

13 years 8 months ago
Bayesian Human Segmentation in Crowded Situations
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 which 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.
Tao Zhao, Ramakant Nevatia
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where CVPR
Authors Tao Zhao, Ramakant Nevatia
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