Creating a Large-scale Synthetic Dataset for Human Activity Recognition

Understand

Creating and labelling datasets of videos for use in training Human Activity Recognition models is an arduous task.

• In this paper, we approach this by using 3D rendering tools to generate a synthetic dataset of videos, and show that a classifier trained on these videos can generalise to real videos.
• We use five different augmentation techniques to generate the videos, leading to a wide variety of accurately labelled unique videos.
• We fine tune a pre-trained I3D model on our videos, and find that the model is able to achieve a high accuracy of 73% on the HMDB51 dataset over three classes.