Make Skeleton-based Action Recognition Model Smaller, Faster and Better

Résumé : Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based action recognition. By using a lightweight network structure (i.e.,~ 0.15 million parameters), DD-Net can reach a super fast speed, as 3,500 FPS on one GPU, or, 2,000 FPS on one CPU. By employing robust features, DD-Net achieves the state-of-the-art performance on our experiment datasets: SHREC (i.e.,~ hand actions) and JHMDB (i.e.,~body actions). Our code will be released with this paper later.