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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 176–200 of 419 papers

TitleStatusHype
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition—0
Object Activity Scene Description, Construction and Recognition—0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition—0
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition—0
Action-Attending Graphic Neural Network—0
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition—0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition—0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition—0
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition—0
Joint Temporal Pooling for Improving Skeleton-based Action Recognition—0
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition—0
Deep Learning on Lie Groups for Skeleton-based Action Recognition—0
ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark—0
Learning Human Activities and Object Affordances from RGB-D Videos—0
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition—0
Neural Graph Matching Networks for Fewshot 3D Action Recognition—0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks—0
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition—0
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition—0
Jointly learning heterogeneous features for rgb-d activity recognition—0
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation—0
Learning stochastic differential equations using RNN with log signature features—0
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition—0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition—0
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