Analyzing human interactions with a network of dynamic probabilistic models

In this paper, we propose a novel method for analyzing human interactions based on the walking trajectories of human subjects. Our principal assumption is that an interaction episode is composed of meaningful smaller unit interactions, which we call `sub-interactions.' The whole interaction is represented by an ordered concatenation or a network of sub-interaction models. From the experiments, we could confirm the effectiveness and robustness of the proposed method by analyzing the internal work of an interaction network and comparing the performance with other previous approaches.