Feature space data augmentation for viewpoint-robust action recognition in videos

The ongoing research on human action recognition models is achieving very promising results, and the existing models reach very high performances. However, they still suffer from one major challenge: their performance decreases on viewpoints not seen in the training step of the model. In this paper, we introduce a new approach based on virtual viewpoint augmentation in the feature space to increase the robustness of the action recognition models to different camera viewpoints. This approach was evaluated on two action recognition datasets: DAHLIA and Toyota SmartHome. Our model shows promising results, with a significant performance increase on both datasets for viewpoints not seen during the training step.