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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 76–100 of 419 papers

TitleStatusHype
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional ConnectivityCode1
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action RecognitionCode1
Skeleton-Contrastive 3D Action Representation LearningCode1
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
UNIK: A Unified Framework for Real-world Skeleton-based Action RecognitionCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily LivingCode1
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action RecognitionCode1
Revisiting Skeleton-based Action RecognitionCode1
Skeleton Aware Multi-modal Sign Language RecognitionCode1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human ActionsCode1
Tensor Representations for Action RecognitionCode1
Spatial Temporal Transformer Network for Skeleton-based Action RecognitionCode1
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action RecognitionCode1
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
Traffic Control Gesture Recognition for Autonomous VehiclesCode1
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
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
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