Stochastic Human Segmentation from a Static Camera
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.
Maintained by
Philippos Mordohai