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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

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Showing 141–150 of 419 papers

TitleStatusHype
Balanced Representation Learning for Long-tailed Skeleton-based Action RecognitionCode0
Pose And Joint-Aware Action RecognitionCode0
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture SearchCode0
Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action RecognitionCode0
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Attack-Augmentation Mixing-Contrastive Skeletal Representation LearningCode0
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition NetworksCode0
Non-Local Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Domain and View-point Agnostic Hand Action RecognitionCode0
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