Synthetic Data Augmentation for Video Action Classification Using Unity

In video analysis, collection and labeling of data can be time and resource-consuming. To solve the scarcity of data problems, synthetic data augmentation is a promising solution. In this paper, we present an approach to generate synthetic videos for action recognition using Unity, the popular game engine. The synthetic videos are generated with high variability in lighting, subjects' models, backgrounds, animations, and camera positions. We use the generated data to augment a small dataset of subjects who are executing physical exercises for action recognition. We tested the augmented data on two state-of-the-art models for action classification and demonstrated the significant benefits of synthetic data augmentation for improving the performance of these models on small datasets in the context of video action recognition.