Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/55343
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Type: Conference paper
Title: Recognizing human activities from silhouettes: Motion subspace and factorial discriminative graphical model.
Author: Wang, L.
Suter, D.
Citation: Proceedings IEEE Computer Vision and Pattern Recognition (CVPR 2007) 2007: www1-8
Publisher: IEEE
Publisher Place: Online
Issue Date: 2007
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 1424411807
9781424411801
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (20th : 2007 : Minneapolis, USA)
Statement of
Responsibility: 
Liang Wang and David Suter
Abstract: We describe a probabilistic framework for recognizing human activities in monocular video based on simple silhouette observations in this paper. The methodology combines kernel principal component analysis (KPCA) based feature extraction and factorial conditional random field (FCRF) based motion modeling. Silhouette data is represented more compactly by nonlinear dimensionality reduction that explores the underlying structure of the articulated action space and preserves explicit temporal orders in projection trajectories of motions. FCRF models temporal sequences in multiple interacting ways, thus increasing joint accuracy by information sharing, with the ideal advantages of discriminative models over generative ones (e.g., relaxing independence assumption between observations and the ability to effectively incorporate both overlapping features and long-range dependencies). The experimental results on two recent datasets have shown that the proposed framework can not only accurately recognize human activities with temporal, intra-and inter-person variations, but also is considerably robust to noise and other factors such as partial occlusion and irregularities in motion styles.
RMID: 0020094086
DOI: 10.1109/CVPR.2007.383298
Appears in Collections:Computer Science publications

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