@inproceedings{2c4f034a3149411783ebdf94651c748d,
title = "Predicting room occupancy with a single Passive infrared (PIR) sensor through behavior extraction",
abstract = "Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants.",
keywords = "Behavior extraction, Monitoring, Occupancy estimation, PIR sensors",
author = "Raykov, {Yordan P.} and Emre Ozer and Ganesh Dasika and Alexis Boukouvalas and Little, {Max A.}",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 ; Conference date: 12-09-2016 Through 16-09-2016",
year = "2016",
month = sep,
day = "12",
doi = "10.1145/2971648.2971746",
language = "English",
series = "UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
publisher = "Association for Computing Machinery",
pages = "1016--1027",
booktitle = "UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
address = "United States",
}