Qinyuan Fang, Maria Kyrarini, Danijela Ristić-Durrant, A. Gräser
{"title":"RGB-D Camera based 3D Human Mouth Detection and Tracking Towards Robotic Feeding Assistance","authors":"Qinyuan Fang, Maria Kyrarini, Danijela Ristić-Durrant, A. Gräser","doi":"10.1145/3197768.3201576","DOIUrl":null,"url":null,"abstract":"In this paper, an RGB-D camera based framework for the recognition and tracking of the human mouth for the purpose of autonomous r obotic feeding is presented. The method employs the state-of-the-art face detection algorithm to acquire the 2D facial landmarks, and the corresponding 3D position of the human mouth is calculated using the depth information. In addition, a 3D point cloud visualizer of the human face with marked facial landmarks is provided for the purpose of visualization. The proposed system is applied in real-time vision-based robot control. Experiments indicate the validity of the proposed work in localising the mouth of different subjects served by the robot with a cup of water.","PeriodicalId":130190,"journal":{"name":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3197768.3201576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
In this paper, an RGB-D camera based framework for the recognition and tracking of the human mouth for the purpose of autonomous r obotic feeding is presented. The method employs the state-of-the-art face detection algorithm to acquire the 2D facial landmarks, and the corresponding 3D position of the human mouth is calculated using the depth information. In addition, a 3D point cloud visualizer of the human face with marked facial landmarks is provided for the purpose of visualization. The proposed system is applied in real-time vision-based robot control. Experiments indicate the validity of the proposed work in localising the mouth of different subjects served by the robot with a cup of water.