Anomaly Classification in People Counting and Occupancy Sensor Systems

Giulia Violatto, Ashish Pandharipande

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

People counting and occupancy sensor systems are used in diverse smart building applications like lighting controls, HVAC controls and workspace management. Anomalies in people counting and occupancy sensor data can result in undesirable application behavior. We address the problem of anomaly classification of sensor data using 3-class random forest classifiers in this work. We specifically consider sensor deployment scenarios where sensor fields-of-view may overlap. Depending on the sensor type, we devise signal features in time, frequency and spatio-temporal domains. The proposed random forest classifiers are evaluated using people counting and binary occupancy sensor data in an office environment, and are shown to have higher true positive rate and a lower false positive rate in comparison to an unsupervised {k} -means method and a random forest classifier with a single signal energy feature.

Original languageEnglish
Article number9017929
Pages (from-to)6573-6581
Number of pages9
JournalIEEE Sensors Journal
Volume20
Issue number12
DOIs
Publication statusPublished - 15 Jun 2020

Bibliographical note

Publisher Copyright:
© 2001-2012 IEEE.

Keywords

  • anomaly classification
  • People counting and occupancy sensors
  • random forest classifiers
  • smart building applications

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