A genetic algorithm approach to human motion capture data segmentation. (May 2014)

• Record Type: Journal Article
• Authors: Lv, Na; Huang, Yan; Feng, Zhiquan; Peng, Jingliang
• Is Part Of: Computer animation and virtual worlds. Volume 25:Number 3/4(2014)
• Page Start: 283
• Page End: 292
• Publication Date: 2014-05
• Subjects: Computer animation -- Periodicals; Visualization -- Periodicals; 006.6
• DOI: 10.1002/cav.1597
• Languages: English
• ISSNs: 1546-4261

Abstract

<abstract abstract-type="main" id="cav1597-abs-0001"> <title>ABSTRACT</title> <p id="cav1597-para-0005">In this paper, we propose a novel genetic algorithm approach to human motion capture (MoCap) data segmentation. For a given MoCap sequence, it constructs a symbolic representation through unsupervised sparse learning, detects the candidate segmenting points to the sequence, models the selection/deselection of each candidate with a gene, and employs the genetic algorithm to find the optimal solution. To the best of our knowledge, we for the first time introduce the genetic algorithm and the sparse learning technique to the problem of MoCap data segmentation, leading to excellent segmentation performance as experimentally demonstrated. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>