Höfinghoff, MaximilianBuschermöhle, RalfKorn, Goy-HinrichSchumacher, MarcelSeipolt, ArneKlein, MaikeKrupka, DanielWinter, CorneliaWohlgemuth, Volker2023-11-292023-11-292023978-3-88579-731-9https://dl.gi.de/handle/20.500.12116/43072Action 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.enAction RecognitionProductionBenchmarkMachine LearningText/Conference Paper10.18420/inf2023_1481617-5468