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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 281290 of 419 papers

TitleStatusHype
Totally Deep Support Vector Machines0
View-Invariant Probabilistic Embedding for Human PoseCode0
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision0
An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition0
PREDICT & CLUSTER: Unsupervised Skeleton Based Action RecognitionCode1
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation0
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural SearchingCode0
Chirality Nets for Human Pose RegressionCode0
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition0
Deep Independently Recurrent Neural Network (IndRNN)Code0
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