Abstract
This paper analyzes the movements of the human body limbs (hands, feet and head) and center of gravity in order to detect and analyze simple actions such as walking and running. We propose a novel vision of the human body, by considering the limbs as cooperative agents that form a hierarchy of cooperative teams: the whole body. The movements are analyzed at individual level and at team level using a modular hierarchical structure. Knowledge of the high-level team actions (such as ldquowalkingrdquo) improves the pertinence of our predictions on the low-level individual actions (foot is moving back and forth) and allows us to compensate for missing or noisy data produced by the feature extraction system. In terms of group behavior recognition, we propose a novel framework for online probabilistic plan recognition in cooperative multiagent systems: the Multiagent Hidden Markov mEmory Model (M-AHMEM), which is a dynamic Bayesian network. Experiments on an existing video database using different models of the human body show the feasibility of the approach.
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