Motion Segmentation of 3D Video using Modified Shape Distribution

Abstract

In this paper, temporal segmentation of 3D video based on motion analysis is presented. 3D video is a sequence of 3D models made for a real-world dynamic object. A modified shape distribution algorithm is proposed to realize stable shape feature representation. In our approach, representative points are generated by clustering vertices based on their spatial distribution instead of randomly sampling vertices as in the original shape distribution algorithm. Motion segmentation is conducted analyzing local minima in degree of motion calculated in the feature vector space. The segmentation algorithm developed in this paper does not require any predefined threshold values but rely on relative relationships among local minima and local maxima of the motion. Therefore, robust segmentation has been achieved. The experiments using 3D video of traditional dances yielded encouraging results with the precision and recall rates of 93% and 88%, respectively, on average