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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 271280 of 419 papers

TitleStatusHype
PYSKL: Towards Good Practices for Skeleton Action Recognition0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition0
Deep Learning on Lie Groups for Skeleton-based Action Recognition0
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation0
Action Recognition with Kernel-based Graph Convolutional Networks0
Recognizing Human Actions as the Evolution of Pose Estimation Maps0
Recovering Complete Actions for Cross-dataset Skeleton Action Recognition0
Totally Deep Support Vector Machines0
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition0
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