In this paper, we present our approach for visual tracking of head, hands and head orientation. Given the images provided by a calibrated stereo-camera, color and disparity information are integrated into a multi-hypotheses tracking framework in order to find the 3D-positions of the respective body parts. Based on the hands' motion, an HMM-based approach is applied to recognize pointing gestures. We show experimentally, that the gesture recognition performance can be improved significantly by using visually gained information about head orientation as an additional feature. Our system aims at applications in the field of human-robot interaction, where it is important to do run-on recognition in real-time, to allow for robot's egomotion and not to rely on manual initialization.
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