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arXiv · 1907.09658

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

Fan Yang·Sakriani Sakti·Yang Wu·Satoshi Nakamura
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Abstract

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 experimental datasets: SHREC (i.e., hand actions) and JHMDB (i.e., body actions). Our code will be released with this paper later.

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Fan Yang, Sakriani Sakti, Yang Wu, Satoshi Nakamura. 2020-03-18. Make Skeleton-based Action Recognition Model Smaller, Faster and Better. https://arxiv.org/abs/1907.09658

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