  Fast and robust hand segmentation and tracking is an essential basis for
gesture recognition and thus an important component for contact-less
human-computer interaction (HCI). Hand gesture recognition based on 2D video
data has been intensively investigated. However, in practical scenarios purely
intensity based approaches suffer from uncontrollable environmental conditions
like cluttered background colors. In this paper we present a real-time hand
segmentation and tracking algorithm using Time-of-Flight (ToF) range cameras
and intensity data. The intensity and range information is fused into one pixel
value, representing its combined intensity-depth homogeneity. The scene is
hierarchically clustered using a GPU based parallel merging algorithm, allowing
a robust identification of both hands even for inhomogeneous backgrounds. After
the detection, both hands are tracked on the CPU. Our tracking algorithm can
cope with the situation that one hand is temporarily covered by the other hand.
