Papers › NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Amir Shahroudy, Jun Liu, Tian-Tsong Ng, Gang Wang
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes. Currently available depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of training samples, distinct class labels, camera views and variety of subjects. In this paper we introduce a large-scale dataset for RGB+D human action recognition with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects. Our dataset contains 60 different action classes including daily, mutual, and health-related actions. In addition, we propose a new recurrent neural network structure to model the long-term temporal correlation of the features for each body part, and utilize them for better action classification. Experimental results show the advantages of applying deep learning methods over state-of-the-art hand-crafted features on the suggested cross-subject and cross-view evaluation criteria for our dataset. The introduction of this large scale dataset will enable the community to apply, develop and adapt various data-hungry learning techniques for the task of depth-based and RGB+D-based human activity analysis.
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | CAD-120 | P-LSTM (5-shot) | Accuracy | 68.1% | #8 of 8 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | Part-aware LSTM | Accuracy (CS) | 62.93 | #128 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | Part-aware LSTM | Accuracy (CV) | 70.27 | #128 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | Deep LSTM | Accuracy (CS) | 60.7 | #130 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | Deep LSTM | Accuracy (CV) | 67.3 | #130 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | Part-Aware LSTM | Accuracy (Cross-Setup) | 26.3% | #83 of 83 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | Part-Aware LSTM | Accuracy (Cross-Subject) | 25.5% | #83 of 83 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | P-LSTM | Accuracy (AV I) | 33% | #4 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | P-LSTM | Accuracy (AV II) | 50% | #4 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | P-LSTM | Accuracy (CS) | 60% | #4 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | P-LSTM | Accuracy (CV I) | 13% | #4 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | P-LSTM | Accuracy (CV II) | 33% | #4 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | LSTM | Accuracy (AV I) | 31% | #7 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | LSTM | Accuracy (AV II) | 68% | #7 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | LSTM | Accuracy (CS) | 56% | #7 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | LSTM | Accuracy (CV I) | 16% | #7 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | LSTM | Accuracy (CV II) | 31% | #7 of 7 | 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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