Using segmented 3D point clouds for accurate likelihood approximation in human pose tracking

The observation likelihood approximation is a central problem in stochastic human pose tracking. In this paper, we present a new approach to quantify the correspondence between hypothetical and observed human poses in depth images. Our approach is based on segmented point clouds, enabling accurate approximations even under self-occlusion and in the absence of color or texture cues. The segmentation step extracts small regions of high saliency such as hands or arms and ensures that the information contained in these regions is not marginalized by larger, less salient regions such as the chest. The proposed approximation function is evaluated on both synthetic and real camera data. In addition, we compare our approximation function against the corresponding function used by a state-of-the-art pose tracker.