{"title":"基于RGB-D数据和2.5D cnn的场景理解和家用机器人自动风险评估","authors":"Rob Dupre, Georgios Tzimiropoulos, V. Argyriou","doi":"10.1109/CVPRW.2017.65","DOIUrl":null,"url":null,"abstract":"In this work the notion of automated risk assessment for 3D scenes is addressed. Using deep learning techniques smart enabled homes and domestic robots can be equipped with the functionality to detect, draw attention to, or mitigate hazards in a given scene. We extend an existing risk estimation framework that incorporates physics and shape descriptors by introducing a novel CNN architecture allowing risk detection at a patch level. Analysis is conducted on RGB-D data and is performed on a frame by frame basis, requiring no temporal information between frames.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"160 1","pages":"476-477"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automated Risk Assessment for Scene Understanding and Domestic Robots Using RGB-D Data and 2.5D CNNs at a Patch Level\",\"authors\":\"Rob Dupre, Georgios Tzimiropoulos, V. Argyriou\",\"doi\":\"10.1109/CVPRW.2017.65\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work the notion of automated risk assessment for 3D scenes is addressed. Using deep learning techniques smart enabled homes and domestic robots can be equipped with the functionality to detect, draw attention to, or mitigate hazards in a given scene. We extend an existing risk estimation framework that incorporates physics and shape descriptors by introducing a novel CNN architecture allowing risk detection at a patch level. Analysis is conducted on RGB-D data and is performed on a frame by frame basis, requiring no temporal information between frames.\",\"PeriodicalId\":6668,\"journal\":{\"name\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"volume\":\"160 1\",\"pages\":\"476-477\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPRW.2017.65\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2017.65","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated Risk Assessment for Scene Understanding and Domestic Robots Using RGB-D Data and 2.5D CNNs at a Patch Level
In this work the notion of automated risk assessment for 3D scenes is addressed. Using deep learning techniques smart enabled homes and domestic robots can be equipped with the functionality to detect, draw attention to, or mitigate hazards in a given scene. We extend an existing risk estimation framework that incorporates physics and shape descriptors by introducing a novel CNN architecture allowing risk detection at a patch level. Analysis is conducted on RGB-D data and is performed on a frame by frame basis, requiring no temporal information between frames.