Cross-View Action Recognition by Projection-Based Augmentation

Challenging issue in cross-view action recognition is the difference between training viewpoint and testing viewpoint. Existing research deals with this problem by transferring knowledge, i.e., finding a viewpoint independent latent space in which action descriptors from different viewpoints are directly comparable. In this paper, we propose a novel approach to tackle the problem based on exploiting the discrimination in action execution through various viewpoints. We take the advantages of depth data to augment viewpoints from an initial camera viewpoint. In our framework, the local motion features and dedicated classifiers are built from the augmented viewpoints. We conduct experiments on the benchmark dataset, the Northwestern-UCLA Multiview Action 3D (N-UCLA3D) dataset. The experimental results indicated that our proposed method leads to outperform the state-of-the-art on the benchmark. In addition, we show the important role of viewpoints to improve the performance of action recognition.