3D human pose estimation from range images with depth difference and geodesic distance

摘要

Depth difference, as a popularly used feature for characterizing pairwise pixels of range images, fails to precisely capture skeleton joints when human body possesses a wild and complicated articulation. As the geodesic distance of pairwise pixels is able to present a global connected property and adjacent pixels often belong to the same body component, we propose an effective and efficient framework for pose estimation from range images. Firstly, all the pixels of a range image are grouped into superpixels using an improved Simple Linear Iterative Clustering algorithm. Secondly, those superpixels are labelled as the components of a human body using the hybrid feature. Thirdly, componentwise cluster feature extraction is undertaken on skeleton joints of body components with K-means clustering algorithm. Finally, the feature points of each component are then stacked as a compact representation of human poses and mapped to the skeleton joints of a human body. Experimental results demonstrate that the proposed framework outperforms several state-of-the-art pose estimation methods. (C) 2019 Elsevier Inc. All rights reserved.