文章基本信息

标题：Unsupervised Posture Modeling and Recognition based on Gaussian Mixture Model and EM Estimation
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作者：xijun, zhu ; Chuanxu, Wang
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2011
卷号：6
期号：8
页码：1445-1451
DOI：10.4304/jsw.6.8.1445-1451
语种：English
出版社：Academy Publisher
摘要：In this paper, we proposed an unsupervised posture modeling method based on Gaussian Mixture Model (GMM). Specifically, each learning posture is described based on its movement features by a set of spatial-temporal interest points (STIPs), salient postures are then clustered from these training samples by an unsupervised algorithm, here we give the comparison of four candidate classification methods and find the optimal one. Furthermore, each clustered posture type is modeled with GMM according to Expectation Maximization (EM) estimation. The experiment results proved that our method can effectively model postures and can be used for posture recognition in video.
关键词：NERF C-means;posture modeling;posture recognition;GMM