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

Abstract

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.

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Cite this paper

@article{Azary2012MultiviewAC, title={Multi-view action classification using sparse representations on Motion History Images}, author={Sherif Azary and Andreas E. Savakis}, journal={2012 Western New York Image Processing Workshop}, year={2012}, pages={5-8} }