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Skeleton Based Action Recognition On Ut
Metrics
Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| Temporal Subspace Clustering | 99.50% | Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning | |
| SCK⊕+DCK⊕ | 99.2 | Tensor Representations for Action Recognition | |
| Complete GR-GCN | 98.5% | Optimized Skeleton-based Action Recognition via Sparsified Graph Regression | - |
| DPRL | 98.5% | Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition | - |
| SCK+DCK | 98.2 | Tensor Representations for Action Recognition | |
| Lie Group | 97.1% | Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group | - |
| GFT | 96% | Graph Based Skeleton Modeling for Human Activity Analysis | - |
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