Graphical modeling and decoding of human actions

This paper presents a graphical model for learning and recognizing human actions. Specifically, we propose to encode actions in a weighted directed graph, referred to as action graph, where nodes of the graph represent salient postures that are used to characterize the actions and shared by all actions. The weight between two nodes measures the transitional probability between the two postures. An action is encoded as one or multiple paths in the action graph. The salient postures are modeled using Gaussian mixture models (GMM). Both the salient postures and action graph are automatically learned from training samples through unsupervised clustering and expectation and maximization (EM) algorithm. Experimental results have verified the performance of the proposed model, its tolerance to noise and viewpoints and its robustness across different subjects and datasets.