{"title":"Cow face detection and recognition based on automatic feature extraction algorithm","authors":"L. Yao, Zexi Hu, Caixing Liu, Hanxing Liu, Yingjie Kuang, Yuefang Gao","doi":"10.1145/3321408.3322628","DOIUrl":null,"url":null,"abstract":"Automatic farm livestock detection and recognition have high importance in the management of livestock due to the increasing potentials in dairy cow welfare as well as production efficiency. In contrast to the general object (e.g., person, car and bird), the recognition of farm livestock still remains challenging due to the open complex scenarios, similar appearance, shape deformation, occlusion and insufficient annotated data and needs to be solved. In this paper, we discuss the problem of cow face detection and recognition by releasing a new large-scale cow dataset which containing about 50,000 annotated cow face detection data and probably 18,000 cow recognition data. Moreover, a cow face recognition framework is proposed which hybrids the detection and recognition model to improve the recognition performance. Experimental results show the superiority of the proposed method. The accuracy of the detection is 98.3%, and the accuracy of the cow face recognition is up to 94.1%.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM Turing Celebration Conference - China","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3321408.3322628","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29
Abstract
Automatic farm livestock detection and recognition have high importance in the management of livestock due to the increasing potentials in dairy cow welfare as well as production efficiency. In contrast to the general object (e.g., person, car and bird), the recognition of farm livestock still remains challenging due to the open complex scenarios, similar appearance, shape deformation, occlusion and insufficient annotated data and needs to be solved. In this paper, we discuss the problem of cow face detection and recognition by releasing a new large-scale cow dataset which containing about 50,000 annotated cow face detection data and probably 18,000 cow recognition data. Moreover, a cow face recognition framework is proposed which hybrids the detection and recognition model to improve the recognition performance. Experimental results show the superiority of the proposed method. The accuracy of the detection is 98.3%, and the accuracy of the cow face recognition is up to 94.1%.