TAD-Net: An Approach for Realtime Action Detection Based on TCN and GCN in Digital Twin Shop-floor

DOI

We proposed a real-time detection approach for shop-floor production action, this approach took the sequence data of continuous human skeleton joints sequence as input, reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN), constructed our Temporal Action Detection Net (TAD-Net), realized real-time shop-floor production action detection.

Identifier
DOI https://doi.org/10.23728/b2share.74fdf3108e0343f786be84fba8592654
Source https://b2share.eudat.eu/records/74fdf3108e0343f786be84fba8592654
Metadata Access https://b2share.eudat.eu/api/oai2d?verb=GetRecord&metadataPrefix=eudatcore&identifier=oai:b2share.eudat.eu:b2rec/74fdf3108e0343f786be84fba8592654
Provenance
Creator Hong, Qing
Publisher EUDAT B2SHARE
Publication Year 2021
Rights Public Domain Dedication (CC Zero); info:eu-repo/semantics/openAccess
OpenAccess true
Contact 1538023886(at)qq.com
Representation
Resource Type Dataset
Format csv; docx; xlsx; rar
Size 108.7 MB; 4 files
Discipline 4.1.12.1 → Computer graphics → Image processing