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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 361370 of 419 papers

TitleStatusHype
Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition NetworksCode0
Tracking Emerges by Colorizing VideosCode0
Balanced Representation Learning for Long-tailed Skeleton-based Action RecognitionCode0
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture SearchCode0
Memory Attention Networks for Skeleton-based Action RecognitionCode0
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual LearningCode0
LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action RecognitionCode0
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action RecognitionCode0
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton DataCode0
Local Spherical Harmonics Improve Skeleton-Based Hand Action RecognitionCode0
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