A human motion feature based on semi-supervised learning of GMM

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• 作者：Tian Qi ; Yinfu Feng ; Jun Xiao ; Hanzhi Zhang ; Yueting Zhuang…
• 关键词：Human motion feature ; Semi ; supervised learning ; Probabilistic model ; Motion retrieval ; Motion classification
• 刊名：Multimedia Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：23
• 期：1
• 页码：85-93
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Operating Systems; Data Storage Representation; Data Encryption; Computer Graphics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-1882
• 卷排序：23

文摘

Using motion capture to create naturally looking motion sequences for virtual character animation has become a standard procedure in the games and visual effects industry. With the fast growth of motion data, the task of automatically annotating new motions is gaining an importance. In this paper, we present a novel statistic feature to represent each motion according to the pre-labeled categories of key-poses. A probabilistic model is trained with semi-supervised learning of the Gaussian mixture model (GMM). Each pose in a given motion could then be described by a feature vector of a series of probabilities by GMM. A motion feature descriptor is proposed based on the statistics of all pose features. The experimental results and comparison with existing work show that our method performs more accurately and efficiently in motion retrieval and annotation.