Establishing affective human robot interaction through contextual information

Determining human intention is a challenging task for establishing affective human robot interaction. The aim of this paper is to provide a vision based framework to achieve a level of understanding about people in an environment before engaging in active communication or interaction. The proposed method combines multiple cues in a Bayesian framework to identify people in the scene and determine potential intentions. To improve the system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. Our results demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment.