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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 181–190 of 419 papers

TitleStatusHype
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
Idempotent Unsupervised Representation Learning for Skeleton-Based Action RecognitionCode0
A Comparative Review of Recent Kinect-based Action Recognition AlgorithmsCode0
Non-Local Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Convolutional Neural Networks on Graphs with Fast Localized Spectral FilteringCode0
Human Action Recognition by Representing 3D Skeletons as Points in a Lie GroupCode0
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural SearchingCode0
Domain and View-point Agnostic Hand Action RecognitionCode0
Multigrid Predictive Filter Flow for Unsupervised Learning on VideosCode0
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