Comparison of Silhouette Shape Descriptors for Example-based Human Pose Recovery
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
View-independent human motion classification using image-based reconstruction
Image and Vision Computing
Viewpoint manifolds for action recognition
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Robust affine invariant shape image retrieval using the ICA Zernike moment shape descriptor
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
View-invariant modeling and recognition of human actions using grammars
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
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Activity recognition in complex scenes can be very challenging because human actions are unconstrained and may be observed from multiple views. While progress has been made in recognizing activities from fixed views, more research is needed in developing view invariant recognition methods. Furthermore, the recognition and classification of activities involves processing data in the space and time domains, which involves large amounts of data and can be computationally expensive to process. To accommodate for view invariance and high dimensional data we propose the use of Manifold Learning using Locality Preserving Projections (LPP). We develop an efficient set of features based on radial distance and present a Manifold Learning framework for learning low dimensional representations of action primitives that can be used to recognize activities at multiple views. Using our approach we present high recognition rates on the Inria IXMAS dataset.