Depth data-driven real-time articulated hand pose recognition

Abstract

This paper presents a fast but robust method to recognize articulated hand pose from single depth images in real-time. We tackle the main challenges in the hand pose recognition, which include the high degree of freedom and self-occlusion of articulated hand motion, using efficient retrieval of a large set of hand pose templates. The normalized orientation templates are used for encoding the depth images containing hand poses, and the locality sensitive hashing is used for finding the nearest neighbors in real time. Our approach does not suffer from the common problems in the conventional tracking approaches such as model initialization and tracking drift, and qualitatively outperforms the existing hand pose estimation techniques.