Skeleton Based Action Recognition On Varying
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Accuracy (AV I)
Accuracy (AV II)
Accuracy (CS)
Accuracy (CV I)
Accuracy (CV II)
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | 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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