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A genetic algorithm approach to human motion capture data segmentation
Author(s) -
Lv Na,
Huang Yan,
Feng Zhiquan,
Peng Jingliang
Publication year - 2014
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1597
Subject(s) - computer science , segmentation , artificial intelligence , genetic algorithm , motion capture , sequence (biology) , representation (politics) , motion (physics) , pattern recognition (psychology) , market segmentation , image segmentation , scale space segmentation , computer vision , machine learning , marketing , biology , politics , political science , law , business , genetics
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 & Sons, Ltd.