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Skeleton Based Action Recognition
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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.
<span style="color:grey; opacity: 0.6">( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action Recognition
Skeleton-based Action Recognition via Adaptive Cross-Form Learning
A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
Skeleton-based Action Recognition via Temporal-Channel Aggregation
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos
PYSKL: Towards Good Practices for Skeleton Action Recognition
ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark
A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints Invariance
FenceNet: Fine-grained Footwork Recognition in Fencing
SpatioTemporal Focus for Skeleton-based Action Recognition
Continual Spatio-Temporal Graph Convolutional Networks
Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition
Bootstrapped Representation Learning for Skeleton-Based Action Recognition
Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition
ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation
Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance
Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition
Self-attention based anchor proposal for skeleton-based action recognition
Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition
Anonymization for Skeleton Action Recognition
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition
Action Recognition with Domain Invariant Features of Skeleton Image
A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
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