{"title":"Daily behavior recognition of cattle based on dynamic region image features in open environment","authors":"Rao Fu, Jiandong Fang, Yudong Zhao","doi":"10.1109/ISPDS56360.2022.9874150","DOIUrl":null,"url":null,"abstract":"In order to recognize the daily behaviors of cattle in an open environment, the daily behaviors of cattle were classified based on the image features in the dynamic region. Firstly, the target detection model was used to locate the cattle feature parts in the dynamic region of the image, and the image features in the dynamic region were extracted according to the label information of the feature parts, then, the deep neural network was used to classify the image features. Finally, the results show that in the open environment, the accuracy of the model in predicting the feeding, lying and standing behaviors of cattle was 84%.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPDS56360.2022.9874150","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 order to recognize the daily behaviors of cattle in an open environment, the daily behaviors of cattle were classified based on the image features in the dynamic region. Firstly, the target detection model was used to locate the cattle feature parts in the dynamic region of the image, and the image features in the dynamic region were extracted according to the label information of the feature parts, then, the deep neural network was used to classify the image features. Finally, the results show that in the open environment, the accuracy of the model in predicting the feeding, lying and standing behaviors of cattle was 84%.