{"title":"Semi-automated computer vision-based tracking of multiple industrial entities: a framework and dataset creation approach","authors":"","doi":"10.1186/s13640-024-00623-6","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>This 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.</p>","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"3 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eurasip Journal on Image and Video Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1186/s13640-024-00623-6","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This 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.
期刊介绍：
EURASIP Journal on Image and Video Processing is intended for researchers from both academia and industry, who are active in the multidisciplinary field of image and video processing. The scope of the journal covers all theoretical and practical aspects of the domain, from basic research to development of application.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
