Cross-view action recognition by cross-domain learning

This paper proposes a novel cross-view human action recognition method by discovering and sharing common knowledge among different video sets captured in multiple viewpoints. We treat a specific view as target domain and the others as source domains and consequently formulate the cross-view action recognition into the cross-domain learning framework. First, the classic bag-of-visual word framework is implemented for visual feature extraction in individual viewpoints. Then, we add two transformation matrices in order to transform original action feature from different views into one common feature space, and also combine the original feature and the transformation feature to proposed the new feature mapping function for target and auxiliary domains respectively. Finally, we proposed a new method to learn the two transformation matrices in model training step based on the standard SVM solver and generate the final classifier for each human action. Extensive experiments are implemented on IXMAS, and TJU. The experimental results demonstrate that the proposed method can consistently outperform the state-of-the-arts.