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http://dx.doi.org/10.3837/tiis.2019.09.017

A Framework for Human Motion Segmentation Based on Multiple Information of Motion Data  

Zan, Xiaofei (Institute of Information Science, Beijing Jiaotong University)
Liu, Weibin (Institute of Information Science, Beijing Jiaotong University)
Xing, Weiwei (School of Software Engineering, Beijing Jiaotong University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.9, 2019 , pp. 4624-4644 More about this Journal
Abstract
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.
Keywords
Motion capture; Human motion; Motion segmentation; Density peak clustering; Time-Series clustering;
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