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 211220 of 419 papers

TitleStatusHype
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action RecognitionCode1
Revisiting Skeleton-based Action RecognitionCode1
Hierarchical growing grid networks for skeleton based action recognitionCode0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition0
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition0
Skeleton Aware Multi-modal Sign Language RecognitionCode1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
Domain and View-point Agnostic Hand Action RecognitionCode0
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition0
NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human ActionsCode1
Show:102550
← PrevPage 22 of 42Next →

No leaderboard results yet.