Stochastic human segmentation from a static camera

Segmenting individual humans in a high-density scene (e.g., a crowd) acquired from a static camera is challenging mainly due to object inter-occlusion. We define this problem as a "model-based segmentation" problem and the solution is obtained using a Markov chain Monte Carlo (MCMC) approach. Knowledge of various aspects including human shape, human height, camera model, and image cues including human head candidates, foreground/background separation are integrated in a Bayesian framework. We show promising results on some challenging data.