Data Fusion for Visual Tracking dedicated to Human-Robot Interaction

Abstract

The interaction between men and machines has become an important topic for the robotics community as it can generalize the use of robots. In this context, advanced robots must integrate capabilities to interpret humans motion as well as persons gestures in order to perform tasks for the humans or in synergy with them. The purpose of this paper is to show a real-time system for face/hand tracking and hand gesture recognition in the particle filtering framework. We introduce mechanisms for visual data fusion within particle filtering to develop trackers combining in a novel way color and shape cues, skin blobs or frontal face detection. For the purpose of face tracking, the fusion of modalities based on color and shape allows to avoid noticeable drift, even possible subsequent loss in the worst case. For gestures interpretation, an extension is proposed to achieve in the tracking loop the recognition of the current hand posture and of its motion in the video stream. In both tracking scenarios, the combination or fusion of cues proves to be more robust in cluttered environments than any of the cues individually. The global performances of the proposed trackers and future works are also discussed.