This paper presents a new posture classification system to analyze different human behaviors directly from video sequences using the technique of triangulation. For well analyzing each posture in the video sequences, we propose a triangulation-based method to triangulate it to different triangle meshes from which two important posture features are then extracted, i.e., the ones of skeleton and centroid context. The first one is used for a coarse search and the second one is for a finer classification to classify postures in more details. For the first descriptor, we take advantages of a dfs (depth-first search) scheme to extract the skeleton features of a posture from its triangulation result. Then, with the help of skeleton information, we can define a new shape descriptor, i.e., centroid context, to describe a posture up to a semantic level. That is, the centroid context is a finer descriptor to describe a posture not only from its whole shape but also from its body parts. Since the two descriptors are complement to each other, all desired human postures can be compared and classified very accurately. The nice ability of posture classification can help us generate a set of key postures for transferring a behavior sequence to a set of symbols. Then, a novel string matching scheme is proposed to analyze different human behaviors. Experimental results have proved that the proposed method is robust, accurate, and powerful in human behavior analysis.