Skeleton Based Action Recognition On Varying
Métriques
Accuracy (AV I)
Accuracy (AV II)
Accuracy (CS)
Accuracy (CV I)
Accuracy (CV II)
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy (AV I) | Accuracy (AV II) | Accuracy (CS) | Accuracy (CV I) | Accuracy (CV II) | Paper Title | Repository |
|---|---|---|---|---|---|---|---|
| SK-CNN | 43% | 77% | 59% | 26% | 68% | Enhanced skeleton visualization for view invariant human action recognition | - |
| TCN | 43% | 64% | 56% | 16% | 43% | Temporal Convolutional Networks for Action Segmentation and Detection | - |
| P-LSTM | 33% | 50% | 60% | 13% | 33% | NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis | - |
| ST-GCN | 53% | 43% | 71% | 25% | 56% | Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition | - |
| Res-TCN | 48% | 68% | 63% | 14% | 48% | Interpretable 3D Human Action Analysis with Temporal Convolutional Networks | - |
| LSTM | 31% | 68% | 56% | 16% | 31% | NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis | - |
| VS-CNN | 57% | 75% | 76% | 29% | 71% | A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition | - |
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