A Survey on Skeleton-Based Activity Recognition using Graph Convolutional Networks (GCN)
Mesafint Fanuel, Xiaohong Yuan, Hyung Nam Kim, Letu Qingge, Kaushik Roy
Abstract
Skeleton-Based Activity recognition is an active research topic in Computer Vision. In recent years, deep learning methods have been used in this area, including Recurrent Neural Network (RNN)-based, Convolutional Neural Network (CNN)-based and Graph Convolutional Network (GCN)-based approaches. This paper provides a survey of recent work on various Graph Convolutional Network (GCN)-based approaches being applied to Skeleton-Based Activity Recognition. We first introduce the conventional implementation of a GCN. Then methods that address the limitations of conventional GCN's are presented.
Topics & Concepts
Convolutional neural networkComputer scienceGraphArtificial intelligenceSkeleton (computer programming)Deep learningRecurrent neural networkPattern recognition (psychology)Machine learningTheoretical computer scienceArtificial neural networkProgramming languageHuman Pose and Action RecognitionContext-Aware Activity Recognition SystemsGait Recognition and Analysis