Sciweavers

Share
CVPR
2007
IEEE

Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model

9 years 9 months ago
Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model
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 ...
Liang Wang, David Suter
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Liang Wang, David Suter
Comments (0)
books