Unsupervised Posture Modeling and Recognition based on Gaussian Mixture Model and EM Estimation
نویسندگان
چکیده
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.
منابع مشابه
A New Algorithm of Posture Modeling and Recognition Based on Gaussian Mixture Model and EM Estimation
In this paper, we proposed a new posture modeling method based on Gaussian Mixture Model (GMM). First, spatial-temporal interest points (STIPs) were extracted according to the properties of human movement, and then, histogram of gradient (HOG) was built to describe the distribution of STIPs in each frame. In addition, the training samples were clustered by non-supervised classification method. ...
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ورودعنوان ژورنال:
- JSW
دوره 6 شماره
صفحات -
تاریخ انتشار 2011