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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 341350 of 419 papers

TitleStatusHype
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild0
Sparse Semi-Supervised Action Recognition with Active Learning0
Action Capsules: Human Skeleton Action Recognition0
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition0
Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition0
Action-Attending Graphic Neural Network0
A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition0
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition0
An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition0
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