A Relational Approach to Content-based Analysis of Motion Capture Data

Motion capture or mocap systems allow for tracking and recording of human motions at high spatial and temporal resolutions. The resulting 3D mocap data is used for motion analysis in fields such as sports sciences, biomechanics, or computer vision, and in particular for motion synthesis in data-driven computer animation. In view of a rapidly growing corpus of motion data, automatic retrieval, annotation, and classification of such data has become an important research field. Since logically similar motions may exhibit significant spatio-temporal variations, the notion of similarity is of crucial importance in comparing motion data streams. After reviewing various aspects of motion similarity, we discuss as the main contribution of this paper a relational approach to content-based motion analysis, which exploits the existence of an explicitly given kinematic model underlying the 3D mocap data. Considering suitable combinations of boolean relations between specified body points allows for capturing the motion content while disregarding motion details. Finally, we sketch how such relational features can be used for automatic and efficient segmentation, indexing, retrieval, classification, and annotation of mocap data.