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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 251–300 of 419 papers

TitleStatusHype
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition—0
Domain and View-point Agnostic Hand Action RecognitionCode0
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition—0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition—0
Action Recognition with Kernel-based Graph Convolutional Networks—0
Temporal Graph Modeling for Skeleton-based Action Recognition—0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition—0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition—0
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action RecognitionCode0
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching—0
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition—0
Pose And Joint-Aware Action RecognitionCode0
Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition—0
Effective Action Recognition with Embedded Key Point Shifts—0
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action RecognitionCode0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks—0
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction—0
Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features—0
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition—0
Hierarchical Action Classification with Network Pruning—0
Context Aware Graph Convolution for Skeleton-Based Action Recognition—0
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition—0
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition—0
Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition—0
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition—0
Temporal Extension Module for Skeleton-Based Action Recognition—0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition—0
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information—0
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition—0
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method—0
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition—0
Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks—0
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition—0
PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition—0
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition—0
Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition—0
Self-Attention Network for Skeleton-based Human Action Recognition—0
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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