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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 291–300 of 419 papers

TitleStatusHype
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction—0
Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition—0
Learning Coupled Spatial-temporal Attention for Skeleton-based Action Recognition—0
Making the Invisible Visible: Action Recognition Through Walls and Occlusions—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition—0
Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference JointsCode0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks—0
Learning Latent Global Network for Skeleton-based Action Prediction—0
Graph Based Skeleton Modeling for Human Activity Analysis—0
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