doi: 10.4304/jsw.6.8.1445-1451
Unsupervised Posture Modeling and Recognition based on Gaussian Mixture Model and EM Estimation
- College of Information Science and Technology, Qingdao University of Science & Technology, Qingdao China
Abstract
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.
Keywords
- NERF
- C-means;
- posture
- modeling;
- posture
How to Cite
Xijun Zhu and Chuanxu Wang, "Unsupervised Posture Modeling and Recognition based on Gaussian Mixture Model and EM Estimation," Journal of Software, vol. 6, no. 8, pp. 1445-1451, 2011. https://doi.org/ 10.4304/jsw.6.8.1445-1451
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).