SOTAVerified

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.

( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )

Papers

Showing 251260 of 419 papers

TitleStatusHype
Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition0
PoTion: Pose MoTion Representation for Action Recognition0
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes0
PYSKL: a toolbox for skeleton-based video understanding0
PYSKL: Towards Good Practices for Skeleton Action Recognition0
Recognizing Human Actions as the Evolution of Pose Estimation Maps0
Recovering Complete Actions for Cross-dataset Skeleton Action Recognition0
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data0
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching0
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition0
Show:102550
← PrevPage 26 of 42Next →

No leaderboard results yet.