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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
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
VPN: Learning Video-Pose Embedding for Activities of Daily LivingCode1
Quo Vadis, Skeleton Action Recognition ?Code1
Subspace Clustering for Action Recognition with Covariance Representations and Temporal PruningCode1
Iterate & Cluster: Iterative Semi-Supervised Action RecognitionCode1
Context Aware Graph Convolution for Skeleton-Based Action Recognition—0
Skeleton-Based Action Recognition With Shift Graph Convolutional NetworkCode1
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
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
Temporal Extension Module for Skeleton-Based Action Recognition—0
Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action RecognitionCode1
Feedback Graph Convolutional Network for Skeleton-based Action Recognition—0
Gimme Signals: Discriminative signal encoding for multimodal activity recognitionCode1
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
Infrared and 3D skeleton feature fusion for RGB-D action recognitionCode1
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
UniPose: Unified Human Pose Estimation in Single Images and VideosCode1
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
PREDICT & CLUSTER: Unsupervised Skeleton Based Action RecognitionCode1
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
Deep Independently Recurrent Neural Network (IndRNN)Code0
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction—0
Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition—0
Learning Coupled Spatial-temporal Attention for Skeleton-based Action Recognition—0
Making the Invisible Visible: Action Recognition Through Walls and Occlusions—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition—0
Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference JointsCode0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks—0
Learning Latent Global Network for Skeleton-based Action Prediction—0
Graph Based Skeleton Modeling for Human Activity Analysis—0
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