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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

Showing 291300 of 419 papers

TitleStatusHype
SpatioTemporal Focus for Skeleton-based Action Recognition0
Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition0
Spatiotemporal graph routing for skeleton-based action recognition0
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition0
Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation0
STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection0
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction0
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition0
Temporal Extension Module for Skeleton-Based Action Recognition0
Temporal Graph Modeling for Skeleton-based Action Recognition0
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