Abstract
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.
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