Assessing the performance of Neural Networks in Recognizing Manual Labor Actions in a Production Environment
| dc.contributor.author | Höfinghoff, Maximilian | |
| dc.contributor.author | Buschermöhle, Ralf | |
| dc.contributor.author | Korn, Goy-Hinrich | |
| dc.contributor.author | Schumacher, Marcel | |
| dc.contributor.author | Seipolt, Arne | |
| dc.contributor.editor | Klein, Maike | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Winter, Cornelia | |
| dc.contributor.editor | Wohlgemuth, Volker | |
| dc.date.accessioned | 2023-11-29T14:50:20Z | |
| dc.date.available | 2023-11-29T14:50:20Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Action recognition technology has gained significant traction in recent years. This paper focuses on evaluating neural network architectures for action recognition in the production industry. By utilizing datasets tailored for production or assembly tasks, various architectures are assessed for their accuracy and performance. The findings of this study provide some insights and guidance for researchers and practitioners to select an appropriate architecture or pretrained models for action recognition in the production industry. | en |
| dc.identifier.doi | 10.18420/inf2023_148 | |
| dc.identifier.isbn | 978-3-88579-731-9 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43072 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2023 - Designing Futures: Zukünfte gestalten | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-337 | |
| dc.subject | Action Recognition | |
| dc.subject | Production | |
| dc.subject | Benchmark | |
| dc.subject | Machine Learning | |
| dc.title | Assessing the performance of Neural Networks in Recognizing Manual Labor Actions in a Production Environment | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 1433 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 1421 | |
| gi.conference.date | 26.-29. September 2023 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | Ökologische Nachhaltigkeit - Zukunft nachhaltig gestalten durch digitalisierte Wertschöpfungsprozesse (DigiWe) |
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