Skeleton Based Action Recognition
Skeleton-based Action Recognition 是一种计算机视觉任务,专注于从传感器捕获的 3D 骨骼关节数据序列中识别和分类人类动作。该任务旨在开发能够理解人体姿态变化并准确判断动作类型的算法,具有广泛的应用前景,包括人机交互、运动分析和安全监控等领域。
CAD-120
Drive&Act
dyalyt
First-Person Hand Action Benchmark
TCN-Summ
Florence 3D
Gaming 3D (G3D)
H2O (2 Hands and Objects)
CHASE(STSA-Net)
HDM05
HMDB51
J-HMBD Early Action
DR^2N
J-HMDB
JHMDB (2D poses only)
HT-ConvNet
JHMDB Pose Tracking
mgPFF+ft 1st
Kinetics-400
STGAT
Kinetics-Skeleton dataset
PoseC3D (SlowOnly-346)
MSR Action3D
Temporal K-Means Clustering + Temporal Subspace Clustering
MSR ActionPairs
Temporal Subspace Clustering
MSRC-12
N-UCLA
SkateFormer
NTU RGB+D
PoseC3D [3D Heatmap]
NTU RGB+D 120
DeGCN
NTU60-X
4s-ShiftGCN
PKU-MMD
SBU / SBU-Refine
Joint Line Distance
SHREC 2017 track on 3D Hand Gesture Recognition
TD-GCN
Skeletics-152
4s-ShiftGCN
Skeleton-Mimetics
MS-G3D
SYSU 3D
SGN
TCG-dataset
Bidirectional LSTM
UAV-Human
HDBN
UCF101
UPenn Action
UNIK
UT-Kinect
Temporal Subspace Clustering
UWA3D
VA-fusion (aug.)
Varying-view RGB-D Action-Skeleton