{"title":"辅助视障人士的AR标记辅助障碍物定位系统","authors":"Xinrui Yu, Guojun Yang, Scott Jones, J. Saniie","doi":"10.1109/EIT.2018.8500166","DOIUrl":null,"url":null,"abstract":"As of in 2017, approximately 3.3% of the people in the world (253 million) are visually impaired. These people face mobility difficulties which impact their quality of life. We propose a system that will assist the visually impaired with their indoor environment mobility. It uses the information from preregistered AR (Augmented Reality) markers to identify specific accessible facilities, such as hallways, restrooms, staircases, and offices. An RGB-D sensor (a sensor that provides color and depth information of every pixel) captures the scene which includes the AR markers. This scene-based information is processed by a neural network to recognize and localize obstacles and accessible facilities. We present the processing algorithm for image and depth profiling. System performance in terms of obstacle localization and recognition was evaluated inside building.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"AR Marker Aided Obstacle Localization System for Assisting Visually Impaired\",\"authors\":\"Xinrui Yu, Guojun Yang, Scott Jones, J. Saniie\",\"doi\":\"10.1109/EIT.2018.8500166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As of in 2017, approximately 3.3% of the people in the world (253 million) are visually impaired. These people face mobility difficulties which impact their quality of life. We propose a system that will assist the visually impaired with their indoor environment mobility. It uses the information from preregistered AR (Augmented Reality) markers to identify specific accessible facilities, such as hallways, restrooms, staircases, and offices. An RGB-D sensor (a sensor that provides color and depth information of every pixel) captures the scene which includes the AR markers. This scene-based information is processed by a neural network to recognize and localize obstacles and accessible facilities. We present the processing algorithm for image and depth profiling. System performance in terms of obstacle localization and recognition was evaluated inside building.\",\"PeriodicalId\":188414,\"journal\":{\"name\":\"2018 IEEE International Conference on Electro/Information Technology (EIT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Electro/Information Technology (EIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EIT.2018.8500166\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Electro/Information Technology (EIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EIT.2018.8500166","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AR Marker Aided Obstacle Localization System for Assisting Visually Impaired
As of in 2017, approximately 3.3% of the people in the world (253 million) are visually impaired. These people face mobility difficulties which impact their quality of life. We propose a system that will assist the visually impaired with their indoor environment mobility. It uses the information from preregistered AR (Augmented Reality) markers to identify specific accessible facilities, such as hallways, restrooms, staircases, and offices. An RGB-D sensor (a sensor that provides color and depth information of every pixel) captures the scene which includes the AR markers. This scene-based information is processed by a neural network to recognize and localize obstacles and accessible facilities. We present the processing algorithm for image and depth profiling. System performance in terms of obstacle localization and recognition was evaluated inside building.