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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.

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

Papers

Showing 251260 of 419 papers

TitleStatusHype
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition0
Domain and View-point Agnostic Hand Action RecognitionCode0
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity0
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition0
Action Recognition with Kernel-based Graph Convolutional Networks0
Temporal Graph Modeling for Skeleton-based Action Recognition0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition0
Sparse Semi-Supervised Action Recognition with Active Learning0
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition0
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