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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 151–175 of 419 papers

TitleStatusHype
Adaptive RNN Tree for Large-Scale Human Action Recognition—0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks—0
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition—0
Modeling Video Evolution for Action Recognition—0
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition—0
Effective Action Recognition with Embedded Key Point Shifts—0
Early action prediction by soft regression—0
Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—0
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve—0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition—0
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System—0
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method—0
Action Capsules: Human Skeleton Action Recognition—0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition—0
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition—0
D numbers theory based game-theoretic framework in adversarial decision making under fuzzy environment—0
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences—0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition—0
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition—0
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning—0
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition—0
Action-Attending Graphic Neural Network—0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition—0
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