Human action recognition using star templates and delaunay triangulation

This paper presents a human action recognition system for recognizing various behaviors directly from videos. Firstly, we triangulate the human body to different triangle meshes. Then, we use a depth-first search (dfs) scheme to find a spanning tree from the set of meshes. All leafs of the spanning tree are adopted as the extremities. Different from traditional approaches to find the extremities on the target's silhouette as skeletons, the extremities found from the internal centroids of triangle meshes can represent a human posture more accurately and robustly. To model each human action, all the input skeleton sequences are then transformed into symbol sequences. Then, we design a string matching scheme to measure the similarity between any two human behaviors. Since 2D postures are used in this paper, the above scheme is sensitive to different view points. To solve the view independent problem, a 2D matrix is then constructed for recording the symbol relations between two viewpoints. Thus, our proposed matching scheme is almost view-invariant. Experimental results show that the proposed scheme is a robust, efficient, and promising tool in human action recognition.