Silhouette-based probabilistic 2D human motion estimation for real-time applications
This paper presents a novel technique for 2D human motion estimation using a single non calibrated camera. The user's five crucial human features (head, hands and feet) are extracted, labeled and tracked, after silhouette segmentation. The crucial points candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region (silhouette) following the silhouette boundary. Selected crucial points are then classified as head, hands or feet using a probabilistic approach weighted by a prior human model. The system can run at 50 Hz paces on standard personal computers.