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基于骨骼的动作识别
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。
NTU RGB+D
PoseC3D [3D Heatmap]
NTU RGB+D 120
CTR-GCN
Kinetics-Skeleton dataset
PoseC3D (SlowOnly-346)
N-UCLA
SGN
J-HMDB
UAV-Human
HDBN
SYSU 3D
SGN
SBU / SBU-Refine
Joint Line Distance
CAD-120
Varying-view RGB-D Action-Skeleton
UT-Kinect
Temporal Subspace Clustering
Florence 3D
JHMDB (2D poses only)
DD-Net
SHREC 2017 track on 3D Hand Gesture Recognition
TD-GCN
MSR Action3D
Temporal K-Means Clustering + Temporal Subspace Clustering
First-Person Hand Action Benchmark
TCN-Summ
PKU-MMD
H2O (2 Hands and Objects)
ISTA-Net
Gaming 3D (G3D)
JHMDB Pose Tracking
mgPFF+ft 1st
UWA3D
VA-fusion (aug.)
UPenn Action
UNIK
NTU60-X
4s-ShiftGCN
Drive&Act
dyalyt
TCG-dataset
Bidirectional LSTM
J-HMBD Early Action
DR^2N
MSRC-12
Skeleton-Mimetics
MS-G3D
Skeletics-152
4s-ShiftGCN
HDM05
HMDB51
UCF101
MSR ActionPairs
Temporal Subspace Clustering
Kinetics-400
STGAT