Unsupervised Posture Modeling Based on Spatial-Temporal Movement Features

Traditional posture modeling for human action recognition is based on silhouette segmentation, which is subject to the noise from illumination variation and posture occlusions and shadow interruptions. In this paper, we extract spatial temporal movement features from human actions and adopt unsuperv...

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Bibliographic Details
Published inAdvanced Research on Electronic Commerce, Web Application, and Communication Vol. 144; pp. 426 - 431
Main Author Yan, ChunJuan
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2011
Springer Berlin Heidelberg
SeriesCommunications in Computer and Information Science
Subjects
GMM
posture modeling
posture recognition
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Summary:Traditional posture modeling for human action recognition is based on silhouette segmentation, which is subject to the noise from illumination variation and posture occlusions and shadow interruptions. In this paper, we extract spatial temporal movement features from human actions and adopt unsupervised clustering method for salient posture learning. First, spatial-temporal interest points (STIPs) were extracted according to the properties of human movement, and then, histogram of gradient was built to describe the distribution of STIPs in each frame for a single pose. In addition, the training samples were clustered by non-supervised classification method. Moreover, the salient postures were modeled with GMM according to Expectation Maximization (EM) estimation. The experiment results proved that our method can effectively and accurately recognize human’s action postures.
ISBN:3642203698
9783642203695
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-642-20370-1_70