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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 121–130 of 419 papers

TitleStatusHype
Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action DetectionCode0
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and DetectionCode0
ADG-Pose: Automated Dataset Generation for Real-World Human Pose EstimationCode0
NTU RGB+D: A Large Scale Dataset for 3D Human Activity AnalysisCode0
On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM NetworksCode0
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action RecognitionCode0
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action RecognitionCode0
Non-Local Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep NetworksCode0
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