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

• Xiaofei Zan,
• Weibin Liu,
• Weiwei Xing,

Vol. 13, No. 9, September 29, 2019
10.3837/tiis.2019.09.017, Download Paper (Free):

• Motion Capture
• Human motion
• motion segmentation
• Density peak clustering
• Time-Series clustering

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.

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