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This paper presents a new technique for action recognition in video using human body part-based approach, combining both local feature description of each body part, and global graphical model structure of the human action. The human body is divided into elementary points from which a Decomposable Triangulated Graph will be built. The temporal variation of human activity is encoded in the velocity distribution of each node in the graph, while the graph structure shows the spatial configuration of all the nodes in the action. Tracking trajectories of unlabeled good feature points are correctly labeled using Maximum a Posterior probability. Dynamic Programming is then implemented to boost up the exhaustive search for the optimal labeling of unknown body parts and the best possible action. A simple and efficient technique for building the optimal structure of the human action graph is also implemented. Experimental results on the KTH dataset proves the success and potential applications of this proposed technique.