Bayesian Approach for Morphology-Based 2-D Human Motion Capture

This paper presents a novel technique for 2D human motion capture using a single non calibrated camera. The user's five extremities (head, hands and feet) are extracted, labeled and tracked after silhouette segmentation. As they are the minimal number of points that can be used in order to enable whole body gestural interaction, we henceforth refer to these features as crucial points. The crucial point candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region that lie on the silhouette boundary. In order to disambiguate the selected crucial points into head, left and right foot and left and right hand classes, we propose a Bayesian framework that combines a MAP approach weighted by a prior model and the intensities of the tracked crucial points. Due to its low computational complexity, the system can run at real-time paces on standard personal computers, with an average error rate range between 2% and 7% in realistic situations, depending on the context and segmentation quality.