{"title":"生命计数:一个无设备的基于csi的建筑物紧急疏散人员计数解决方案","authors":"Daniel Konings, F. Alam","doi":"10.1109/SAS48726.2020.9220032","DOIUrl":null,"url":null,"abstract":"During large scale building evacuations, it is difficult to ascertain how many people have left the premises safely. To assist in the rescue effort, indoor counting solutions can provide emergency personnel with the number of people who have evacuated the building, and from which floors. LifeCount implements a novel two stage neural network-based algorithm to accurately count the number of people passing through a hallway. Experimental results show that LifeCount can attain a zero counting error accuracy of 96.9%.","PeriodicalId":223737,"journal":{"name":"2020 IEEE Sensors Applications Symposium (SAS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"LifeCount: A Device-free CSI-based Human Counting Solution for Emergency Building Evacuations\",\"authors\":\"Daniel Konings, F. Alam\",\"doi\":\"10.1109/SAS48726.2020.9220032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"During large scale building evacuations, it is difficult to ascertain how many people have left the premises safely. To assist in the rescue effort, indoor counting solutions can provide emergency personnel with the number of people who have evacuated the building, and from which floors. LifeCount implements a novel two stage neural network-based algorithm to accurately count the number of people passing through a hallway. Experimental results show that LifeCount can attain a zero counting error accuracy of 96.9%.\",\"PeriodicalId\":223737,\"journal\":{\"name\":\"2020 IEEE Sensors Applications Symposium (SAS)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Sensors Applications Symposium (SAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAS48726.2020.9220032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Sensors Applications Symposium (SAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAS48726.2020.9220032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
LifeCount: A Device-free CSI-based Human Counting Solution for Emergency Building Evacuations
During large scale building evacuations, it is difficult to ascertain how many people have left the premises safely. To assist in the rescue effort, indoor counting solutions can provide emergency personnel with the number of people who have evacuated the building, and from which floors. LifeCount implements a novel two stage neural network-based algorithm to accurately count the number of people passing through a hallway. Experimental results show that LifeCount can attain a zero counting error accuracy of 96.9%.