Papers › Skeleton-based Action Recognition with Convolutional Neural Networks
Skeleton-based Action Recognition with Convolutional Neural Networks
Chao Li, Qiaoyong Zhong, Di Xie, ShiLiang Pu
Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN). In this paper, we propose a novel convolutional neural networks (CNN) based framework for both action classification and detection. Raw skeleton coordinates as well as skeleton motion are fed directly into CNN for label prediction. A novel skeleton transformer module is designed to rearrange and select important skeleton joints automatically. With a simple 7-layer network, we obtain 89.3% accuracy on validation set of the NTU RGB+D dataset. For action detection in untrimmed videos, we develop a window proposal network to extract temporal segment proposals, which are further classified within the same network. On the recent PKU-MMD dataset, we achieve 93.7% mAP, surpassing the baseline by a large margin.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | NTU RGB+D | CNN+Motion+Trans | Accuracy (CS) | 83.2 | #102 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | CNN+Motion+Trans | Accuracy (CV) | 89.3 | #102 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | PKU-MMD | Li et al. [[Li et al.2017b]] | mAP@0.50 (CS) | 90.4 | #3 of 4 | Archive leaderboard | report |
| Skeleton Based Action Recognition | PKU-MMD | Li et al. [[Li et al.2017b]] | mAP@0.50 (CV) | 93.7 | #3 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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