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.
Bayesian human segmentation in crowded situations
2003-01-01
726818 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Bayesian Human Segmentation in Crowded Situations
British Library Conference Proceedings | 2003
|Occlusion Vehicle Segmentation Algorithm in Crowded Scene for Traffic Surveillance System
Springer Verlag | 2018
|Online Contents | 1998
Three-Dimensional Model-Based Human Detection in Crowded Scenes
Online Contents | 2012
|MONITORING CROWDED TRAFFIC SCENES
British Library Conference Proceedings | 2002
|