Human Body Tracking with Auxiliary Measurements
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
A video surveillance system under varying environmental conditions
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
An adaptive focus-of-attention model for video surveillance and monitoring
Machine Vision and Applications
Recovery of upper body poses in static images based on joints detection
Pattern Recognition Letters
Face detection using multiple cues
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Adaptive model for robust pedestrian counting
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Computer Vision and Image Understanding
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Segmenting individual humans in a high-density scene (e.g.,a crowd) acquired from a static camera is challenging mainlydue to object inter-occlusion (Fig.1). We definethis problem asa "model-basedsegmentation"problem and the solution is obtainedusing a Markov chain Monte Carlo (MCMC) approach.Knowledge of various aspects including human shape, humanheight, camera model, and image cues including human headcandidates, foreground/background separation are integratedin a Bayesian framework. We show promising results on somechallenging data.