A Framework for Human Motion Segmentation Based on Multiple Information of Motion Data
Xiaofei Zan, Weibin Liu, Weiwei Xing · KSII Transactions on Internet and Information Systems · 2019
With the development of films, games and animation industry, analysis and reuse of human motion capture data become more and more important.Human motion segmentation, which divides a long motion sequence into different types of fragments, is a key part of mocap-based techniques.However, most of the segmentation methods only take into account low-level physical information (motion characteristics) or high-level data information (statistical characteristics) of motion data.They cannot use the data information fully.In this paper, we propose an unsupervised framework using both low-level physical information and high-level data information of human motion data to solve the human segmentation problem.First, we introduce the algorithm of CFSFDP and optimize it to carry out initial segmentation and obtain a good result quickly.Second, we use the ACA method to perform optimized segmentation for improving the result of segmentation.The experiments demonstrate that our framework has an excellent performance.