{"title":"基于改进的ResNet50的人体姿态估计","authors":"Xiao Xiao, W. Wan","doi":"10.1049/CP.2017.0126","DOIUrl":null,"url":null,"abstract":"This paper provides a method to predict 2D human pose in an image based on deep model ResNet-50. Human pose estimation is formulated as a regression problem towards body joints through top-down methods. First, we detect the position of humans in holistic image. Then, we take advantages of multi-stages cascade of ResNet-50 to reason about human body joints position. Our approach on challenging the FLIC datasets with large pose variation outperforms the state-of-the-art methods on these benchmarks.","PeriodicalId":424212,"journal":{"name":"4th International Conference on Smart and Sustainable City (ICSSC 2017)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Human pose estimation via improved ResNet50\",\"authors\":\"Xiao Xiao, W. Wan\",\"doi\":\"10.1049/CP.2017.0126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper provides a method to predict 2D human pose in an image based on deep model ResNet-50. Human pose estimation is formulated as a regression problem towards body joints through top-down methods. First, we detect the position of humans in holistic image. Then, we take advantages of multi-stages cascade of ResNet-50 to reason about human body joints position. Our approach on challenging the FLIC datasets with large pose variation outperforms the state-of-the-art methods on these benchmarks.\",\"PeriodicalId\":424212,\"journal\":{\"name\":\"4th International Conference on Smart and Sustainable City (ICSSC 2017)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"4th International Conference on Smart and Sustainable City (ICSSC 2017)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1049/CP.2017.0126\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"4th International Conference on Smart and Sustainable City (ICSSC 2017)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/CP.2017.0126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper provides a method to predict 2D human pose in an image based on deep model ResNet-50. Human pose estimation is formulated as a regression problem towards body joints through top-down methods. First, we detect the position of humans in holistic image. Then, we take advantages of multi-stages cascade of ResNet-50 to reason about human body joints position. Our approach on challenging the FLIC datasets with large pose variation outperforms the state-of-the-art methods on these benchmarks.
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