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 261270 of 419 papers

TitleStatusHype
Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition0
PoTion: Pose MoTion Representation for Action Recognition0
Early action prediction by soft regression0
Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition0
Learning Coupled Spatial-temporal Attention for Skeleton-based Action Recognition0
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes0
D numbers theory based game-theoretic framework in adversarial decision making under fuzzy environment0
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences0
PYSKL: a toolbox for skeleton-based video understanding0
Show:102550
← PrevPage 27 of 42Next →

No leaderboard results yet.