Myke D. M. Valadão, Diego A. Amoedo, Gustavo M. Torres, E. V. C. U. Mattos, Antônio M. C. Pereira, Matheus S. Uchôa, Lucas M. Torres, Victor L. G. Cavalcante, José E. B. S. Linhares, M. O. Silva, Agemilson P. Silva, Caio F. S. Cruz, Rômulo Fabrício, Ruan J. S. Belém, Thiago B. Bezerra, W. S. S. Júnior, Celso B. Carvalho
{"title":"Automatic Video Labeling with Assembly Actions of Workers on a Production Line Using ResNet","authors":"Myke D. M. Valadão, Diego A. Amoedo, Gustavo M. Torres, E. V. C. U. Mattos, Antônio M. C. Pereira, Matheus S. Uchôa, Lucas M. Torres, Victor L. G. Cavalcante, José E. B. S. Linhares, M. O. Silva, Agemilson P. Silva, Caio F. S. Cruz, Rômulo Fabrício, Ruan J. S. Belém, Thiago B. Bezerra, W. S. S. Júnior, Celso B. Carvalho","doi":"10.1109/ICCE-Taiwan55306.2022.9869008","DOIUrl":null,"url":null,"abstract":"In this work, conducted by two partners, called UFAM/CETELI and, Envision (TPV Group), we present a method of automatic labeling of frames of worker's actions in factory environments using a model generated by a residual neural network. With this approach we used some manually labeled frames to training a model that provide the label of 4 classes of actions. We achieve accuracy rate over 96%, which give reliability to a supervised training of 3D dataset of actions.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Consumer Electronics - Taiwan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, conducted by two partners, called UFAM/CETELI and, Envision (TPV Group), we present a method of automatic labeling of frames of worker's actions in factory environments using a model generated by a residual neural network. With this approach we used some manually labeled frames to training a model that provide the label of 4 classes of actions. We achieve accuracy rate over 96%, which give reliability to a supervised training of 3D dataset of actions.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
