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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 331340 of 419 papers

TitleStatusHype
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition0
Skeleton-Based Online Action Prediction Using Scale Selection Network0
Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition0
Skeleton-based Action Recognition of People Handling Objects0
Motion feature augmented network for dynamic hand gesture recognition from skeletal data0
Graph Neural Networks with convolutional ARMA filtersCode0
Cross-modal Learning by Hallucinating Missing Modalities in RGB-D VisionCode0
Action Machine: Rethinking Action Recognition in Trimmed Videos0
Structure-Aware Convolutional Neural NetworksCode0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression0
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