Vision-based attention estimation and selection for social robot to perform natural interaction in the open world

In this paper, a novel vision system is proposed to estimate attention of people from rich visual clues for social robot to perform natural interactions with multiple participants in public environments. The vision detection and recognition modules include multi-person detection and tracking, upper-body pose recognition, face and gaze detection, lip motion analysis for speaking recognition, and facial expression recognition. A computational approach is proposed to generate a quantitative estimation of human attention. The vision system is implemented on a robotic receptionist "EVE" and encouraging results have been obtained.