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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 351360 of 419 papers

TitleStatusHype
Unsupervised representation learning with long-term dynamics for skeleton based action recognition0
Memory Attention Networks for Skeleton-based Action RecognitionCode0
View Adaptive Neural Networks for High Performance Skeleton-based Human Action RecognitionCode0
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNNCode0
Learning clip representations for skeleton-based 3d action recognition0
Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition0
Glimpse Clouds: Human Activity Recognition from Unstructured Feature PointsCode0
Relational Autoencoder for Feature ExtractionCode0
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences0
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