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Multi-view action classification using sparse representations on Motion History Images

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2 Author(s)
Sherif Azary ; Computing and Information Sciences and Computer Engineering Rochester Institute of Technology, Rochester, NY 14623 ; Andreas Savakis

Multi-view action classification is an important component of real world applications such as automatic surveillance and sports analysis. Motion History Images capture the location and direction of motion in a scene and sparse representations provide a compact representation of high dimensional signals. In this paper, we propose a multi-view action classification algorithm based on sparse representation of spatio-temporal action representations using motion history images. We find that this approach is effective at multi-view action classification and experiments with the i3DPost Multi-view Dataset achieve high classification rates.

Published in:

Image Processing Workshop (WNYIPW), 2012 Western New York

Date of Conference:

9-9 Nov. 2012