Multimodal Activity Recognition On Ev Action
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| ST-GCN (Skeleton Kinect) | 79.6 | Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition | |
| ST-GCN (Skeleton Vicon) | 50.7 | Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition | |
| TCN (Skeleton Kinect) | 80.1 | Interpretable 3D Human Action Analysis with Temporal Convolutional Networks | |
| WHDMM (Depth) | 40.2 | Action recognition from depth maps using deep convolutional neural networks | - |
| TCN (Skeleton Vicon) | 64.1 | Interpretable 3D Human Action Analysis with Temporal Convolutional Networks | |
| LSTM-FFT (EMG) | 44.1 | EV-Action: Electromyography-Vision Multi-Modal Action Dataset | |
| TCN-RMS (Skeleton Kinect+EMG) | 67.4 | EV-Action: Electromyography-Vision Multi-Modal Action Dataset | |
| TSN (RGB) | 73.6 | Temporal Segment Networks: Towards Good Practices for Deep Action Recognition | |
| TCN-FFT (Skeleton Vicon+EMG) | 64.4 | EV-Action: Electromyography-Vision Multi-Modal Action Dataset |
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