Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Benchmark Model Rank Results
action-recognition-on-h2o-2-hands-and-objectsST-GCN#9Actions Top-1: 73.86RGB: NoHand Pose: 3DObject Pose: Yes
skeleton-based-action-recognition-on-ntu-rgbd-120ST-GCN [PYSKL, 3D Skeleton]#40Accuracy (Cross-Subject): 86.2Accuracy (Cross-Setup): 88.4
skeleton-based-action-recognition-on-ntu-rgbd-120ST-GCN [PYSKL, 2D Skeleton]#45Accuracy (Cross-Subject): 84.7Accuracy (Cross-Setup): 89.0
skeleton-based-action-recognition-on-ntu-rgbd-60ST-GCN [PYSKL, 3D Skeleton]#45Accuracy (CS): 90.7Accuracy (CV): 96.5
skeleton-based-action-recognition-on-ntu-rgbd-60ST-GCN [Vanilla, 2D Skeleton]#47Accuracy (CS): 90.1Accuracy (CV): 95.1
skeleton-based-action-recognition-on-ntu-rgbd-60ST-GCN [Vanilla, 3D Skeleton]#67Accuracy (CS): 86.6Accuracy (CV): 93.2
skeleton-based-action-recognition-on-ntu-rgbd-60ST-GCN#84Accuracy (CS): 81.5Accuracy (CV): 88.3
skeleton-based-action-recognition-on-uavST-GCN#7CSv1(%): 30.25CSv2(%): 56.14