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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 201–225 of 419 papers

TitleStatusHype
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition—0
IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition—0
Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features—0
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition—0
Joint Action Recognition and Pose Estimation From Video—0
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition—0
Jointly learning heterogeneous features for rgb-d activity recognition—0
Joint Temporal Pooling for Improving Skeleton-based Action Recognition—0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition—0
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition—0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition—0
Learning clip representations for skeleton-based 3d action recognition—0
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition—0
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition—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
Learning Latent Global Network for Skeleton-based Action Prediction—0
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition—0
Learning Shape-Motion Representations from Geometric Algebra Spatio-Temporal Model for Skeleton-Based Action 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
LLMs are Good Action Recognizers—0
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition—0
Making the Invisible Visible: Action Recognition Through Walls and Occlusions—0
MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning—0
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