Using Bayes and Image Morphology for Markerless Human Motion Capture

This work 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, labelled and tracked after silhouette segmentation. Since those are the minimal number of points that can be used in order to enable whole body gestural interaction, we will call these features the silhouette's 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 which lie on the silhouette boundary. In order to disambiguate the selected crucial points into head, left and right foot, left and right hand classes, we propose a Bayesian method that combines a prior human model and additional information extracted from the silhouette using image-morphology techniques. 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.

Autorentext

was born in Madrid (Spain) in 1977. In June 2002 he graduated in Electrical Engineering at the UCL (Belgium). He obtained a PhD degree from the UCL in collaboration with the UPC (Barcelona) in June 2006. Some of his other passions are Computer Graphics animation, movie reviews, blues piano, literature and his family.