F. Flores, Sirajum Munir, Matias Quintana, Anand P. Krishnan, M. Berges
{"title":"Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor","authors":"F. Flores, Sirajum Munir, Matias Quintana, Anand P. Krishnan, M. Berges","doi":"10.1145/3359427.3361916","DOIUrl":null,"url":null,"abstract":"Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.","PeriodicalId":267440,"journal":{"name":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3359427.3361916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.