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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 291–300 of 419 papers

TitleStatusHype
Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action RecognitionCode0
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional NetworksCode0
Totally Deep Support Vector Machines—0
View-Invariant Probabilistic Embedding for Human PoseCode0
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision—0
An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition—0
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation—0
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural SearchingCode0
Chirality Nets for Human Pose RegressionCode0
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition—0
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