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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 131–140 of 419 papers

TitleStatusHype
On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM NetworksCode0
NTU RGB+D: A Large Scale Dataset for 3D Human Activity AnalysisCode0
Expressive Keypoints for Skeleton-based Action Recognition via Skeleton TransformationCode0
Bayesian Hierarchical Dynamic Model for Human Action RecognitionCode0
Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM NetworksCode0
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
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-SupervisionCode0
Balanced Representation Learning for Long-tailed Skeleton-based Action RecognitionCode0
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