Interprétation de Mouvements Temps Réel

This paper proposes a new method for real-time human motion recognition using Motion Capture (MoCap) data. In particular, our method uses examplars to learn actions, without the need for learning intra class variations. We have formalized streamed recognizable actions, coming from an online MoCap engine, into a motion graph, similar to an animation motion graph. This graph is used as an automaton to recognize known actions as well as to add new ones. We have defined and used a spatio-temporal metric for similarity measurements to achieve more accurate feedbacks on classification. The proposed method has the advantage of being linear and incremental, making the recognition process very fast and the addition of a new action straightforward. Furthermore, actions can be recognized with a score, even before they are fully completed. Thanks to the use of a skeleton-centric coordinate system, our recognition method has become view-invariant. We have successfully tested our action recognition method on both synthetic and real data. The latter was obtained from live input videos using either our online markerless MoCap engine or the Kinect acquisition system.