Skeleton Based Action Recognition On Florence
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| Deep STGC_K | 99.1% | Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition |
| Complete GR-GCN | 98.4% | Optimized Skeleton-based Action Recognition via Sparsified Graph Regression |
| SCK⊕+DCK⊕ | 97.45 | Tensor Representations for Action Recognition |
| Temporal Spectral Clustering + Temporal Subspace Clustering | 95.81% | Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning |
| SCK+DCK | 95.23 | Tensor Representations for Action Recognition |
| Rolling Rotations (FTP) | 91.40% | Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data |
| Lie Group | 90.9% | Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group |
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