Human Body Part Labeling and Tracking Using Graph Matching Theory

International audienceProperly labeling human body parts in video sequencesis essential for robust tracking and motion interpretationframeworks. We propose to perform this task by usingGraph Matching. The silhouette skeleton is computed anddecomposed into a set of segments corresponding to the differentlimbs. A Graph capturing the topology of the segmentsis generated and matched against a 3D model of thehuman skeleton. The limb identification is carried out foreach node of the graph, potentially leading to the absenceof correspondence. The method captures the minimal informationabout the skeleton shape. No assumption about theviewpoint, the human pose, the geometry or the appearenceof the limbs is done during the matching process, making theapproach applicable to every configuration. Some correspondancesthat might be ambiguous only relying on topologyare enforced by tracking each graph node over time.Several results present the efficiency of the labeling, particularlyits robustness to limb detection errors that are likelyto occur in real situations because of occlusions or low levelsystem failures. Finally the relevance of the labeling in anoverall tracking system is pointed out