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Skeleton Based Action Recognition On Gaming
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||
|---|---|---|
| CNN | 96.0 | Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks |
| Temporal K-Means Clustering + Temporal Covariance Subspace Clustering | 92.91% | Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning |
| HDM-BG | 92.0 | Bayesian Hierarchical Dynamic Model for Human Action Recognition |
| Rolling Rotations (FTP) | 90.94 | Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data |
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