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Skeleton Based Action Recognition
Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.
<span style="color:grey; opacity: 0.6">( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )</span>
데이터셋
34개NTU RGB+DNTU RGB+D 120Kinetics-Skeleton datasetN-UCLAJ-HMDBSBU / SBU-RefineSYSU 3DUAV-HumanCAD-120Florence 3DSHREC 2017 track on 3D Hand Gesture RecognitionUT-KinectVarying-view RGB-D Action-SkeletonJHMDB (2D poses only)First-Person Hand Action BenchmarkGaming 3D (G3D)H2O (2 Hands and Objects)MSR Action3DPKU-MMDJHMDB Pose TrackingNTU60-XUPenn ActionUWA3DDrive&ActHMDB51J-HMBD Early ActionMSRC-12TCG-datasetHDM05Kinetics-400