Curvature-based Human Body Parts Segmentation in Physiotherapy

Abstract

Analysing human sports activity in computer vision requires reliable segmentation of the human body into meaningful parts, such as arms, torso and legs. Therefore, we present a novel strategy for human body segmentation. Firstly, greyscale images of human bodies are divided into smooth intensity patches with an adaptive region growing algorithm based on low-degree polynomial fitting. Then, the key idea in this paper is that human body parts are approximated by nearly cylindrical surfaces, of which the axes of minimum curvature accurately reconstruct the human body skeleton. Next, human body segmentation is qualitatively evaluated with a line segment distance between reconstructed human body skeletons and ground truth skeletons. When compared with human body parts segmentations based on mean shift, normalized cuts and watersheds, the proposed method achieves more accurate segmentations and better reconstructions of human body skeletons.