A Robust Method for Human Pose Estimation Based on Geodesic Distance Features

In this work, we propose a real-time capable and robust method for human pose estimation based on geodesic distance features from depth images. Although a lot of work has been done in the field of the human pose estimation, it remains a challenging task - especially because of the high variability of human poses and self occlusions. The pose estimation focuses on the upper body, as it is the relevant part for a subsequent gesture and posture recognition and therefore the basis for a real human-machine-interaction. A graph-based representation of the 3D point cloud data is determined which allows for the measurement of pose-independent geodesic distances on the surface of the body. Based on these distances we determine feature points that are used for the adaptation of a kinematic skeleton model of the human upper body. The method does not need any pre-trained pose classifiers and can therefore track arbitrary poses as long as the user is not turned away from the camera.