Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction

Yordan P. Raykov, Emre Ozer, Ganesh Dasika, Alexis Boukouvalas, Max A. Little

Research output: Chapter in Book/Published conference outputConference publication

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.

Original languageEnglish
Title of host publicationUbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Place of PublicationNew York, NY (US)
PublisherACM
Pages1016-1027
Number of pages12
ISBN (Print)978-1-4503-4461-6
DOIs
Publication statusPublished - 12 Sep 2016
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing - Heidelberg, Germany
Duration: 12 Sep 201616 Sep 2016

Conference

Conference2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Abbreviated titleUbiComp 2016
CountryGermany
CityHeidelberg
Period12/09/1616/09/16

Bibliographical note

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Keywords

  • behavior extraction
  • monitoring
  • occupancy estimation
  • PIR sensors

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