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