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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 376–400 of 419 papers

TitleStatusHype
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition—0
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks—0
Hierarchical recurrent neural network for skeleton based action recognition—0
Hierarchical Action Classification with Network Pruning—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
HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition—0
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition—0
10,000+ Times Accelerated Robust Subset Selection (ARSS)—0
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics—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
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction—0
Learning Shape-Motion Representations from Geometric Algebra Spatio-Temporal Model for Skeleton-Based Action Recognition—0
Adversarial Attack on Skeleton-based Human 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
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition—0
Graph Based Skeleton Modeling for Human Activity Analysis—0
LLMs are Good Action Recognizers—0
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction—0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition—0
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