Semi-automated computer vision-based tracking of multiple industrial entities

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dc.contributor.authorRutinowski, Jérôme
dc.contributor.authorYoussef, Hazem
dc.contributor.authorFranke, Sven
dc.contributor.authorPriyanta, Irfan Fachrudin
dc.contributor.authorPolachowski, Frederik
dc.contributor.authorRoidl, Moritz
dc.contributor.authorReining, Christopher
dc.date.accessioned2025-06-05T07:54:56Z
dc.date.available2025-06-05T07:54:56Z
dc.date.issued2024-03-22
dc.description.abstractThis contribution presents the TOMIE framework (Tracking Of Multiple Industrial Entities), a framework for the continuous tracking of industrial entities (e.g., pallets, crates, barrels) over a network of, in this example, six RGB cameras. This framework makes use of multiple sensors, data pipelines, and data annotation procedures, and is described in detail in this contribution. With the vision of a fully automated tracking system for industrial entities in mind, it enables researchers to efficiently capture high-quality data in an industrial setting. Using this framework, an image dataset, the TOMIE dataset, is created, which at the same time is used to gauge the framework’s validity. This dataset contains annotation files for 112,860 frames and 640,936 entity instances that are captured from a set of six cameras that perceive a large indoor space. This dataset out-scales comparable datasets by a factor of four and is made up of scenarios, drawn from industrial applications from the sector of warehousing. Three tracking algorithms, namely ByteTrack, Bot-Sort, and SiamMOT, are applied to this dataset, serving as a proof-of-concept and providing tracking results that are comparable to the state of the art.en
dc.identifier.urihttp://hdl.handle.net/2003/43727
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25501
dc.language.isoen
dc.relation.ispartofseriesEURASIP journal on image and video processing; 2024
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectWarehousingen
dc.subjectComputer visionen
dc.subjectObject detectionen
dc.subjectClassificationen
dc.subject.ddc620
dc.subject.ddc670
dc.subject.rswkLagerhaltung
dc.subject.rswkMaschinelles Sehen
dc.subject.rswkObjekterkennung
dc.subject.rswkAutomatische Klassifikation
dc.subject.rswkIndustrie 4.0
dc.titleSemi-automated computer vision-based tracking of multiple industrial entitiesen
dc.title.alternativea framework and dataset creation approachen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationRutinowski, J., Youssef, H., Franke, S. et al. Semi-automated computer vision-based tracking of multiple industrial entities: a framework and dataset creation approach. J Image Video Proc. 2024, 8 (2024). https://doi.org/10.1186/s13640-024-00623-6
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1186/s13640-024-00623-6

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