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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 371–380 of 419 papers

TitleStatusHype
Action-Attending Graphic Neural Network—0
3D CNNs on Distance Matrices for Human Action RecognitionCode0
Adaptive RNN Tree for Large-Scale Human Action Recognition—0
RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in VideosCode0
Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM NetworksCode0
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition—0
Enhanced skeleton visualization for view invariant human action recognition—0
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks—0
Skeleton-based Action Recognition Using LSTM and CNN—0
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition—0
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