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DC poleHodnotaJazyk
dc.contributor.authorSturm, Fabian
dc.contributor.authorSathiyababu, Rahul
dc.contributor.authorHergenroether, Elke
dc.contributor.authorSiegel, Melanie
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-18T15:50:22Z
dc.date.available2023-10-18T15:50:22Z
dc.date.issued2023
dc.identifier.citationWSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 340-350.en
dc.identifier.isbn978-80-86943-32-9
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/54442
dc.format11 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectrozpoznávání lidského jednánícs
dc.subjectprůmyslová montážcs
dc.subjectpolořízené učenícs
dc.subjectpřenos učenícs
dc.subjecttransformátorcs
dc.titleSemi-Supervised Learning Approach for Fine Grained Human Hand Action Recognition in Industrial Assemblyen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedUntil now, it has been impossible to imagine industrial manual assembly without humans due to their flexibility and adaptability. But the assembly process does not always benefit from human intervention. The error-proneness of the assembler due to disturbance, distraction or inattention requires intelligent support of the employee and is ideally suited for deep learning approaches because of the permanently occurring and repetitive data patterns. However, there is the problem that the labels of the data are not always sufficiently available. In this work, a spatio-temporal transformer model approach is used to address the circumstances of few labels in an industrial setting. A pseudo-labeling method from the field of semi-supervised transfer learning is applied for model training, and the entire architecture is adapted to the fine-grained recognition of human hand actions in assembly. This implementation significantly improves the generalization of the model during the training process over different variations of strong and weak classes from the ground truth and proves that it is possible to work with deep learning technologies in an industrial setting, even with few labels. In addition to the main goal of improving the generalization capabilities of the model by using less data during training and exploring different variations of appropriate ground truth and new classes, the recognition capabilities of the model are improved by adding convolution to the temporal embedding layer, which increases the test accuracy by over 5% compared to a similar predecessor model.en
dc.subject.translatedhuman action recognitionen
dc.subject.translatedindustrial assemblyen
dc.subject.translatedsemi-supervised learningen
dc.subject.translatedtransfer learningen
dc.subject.translatedtransformeren
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3301.58
dc.type.statusPeer-revieweden
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