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Towards robust device-free passive localization through automatic camera-assisted recalibration

  • Chenren Xu
  • , Mingchen Gao
  • , Bernhard Firner
  • , Yanyong Zhang
  • , Richard Howard
  • , Jun Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

Abstract

Device-free passive localization (DfP) techniques can localize human subjects without wearing a radio tag. Being convenient and private, DfP can find many applications in ubiquitous/pervasive computing. Unfortunately, DfP techniques need frequent manual recalibration of the radio signal values, which can be cumbersome and costly. We present SenCam, a sensor-camera collaboration solution that conducts automatic recalibration by leveraging existing surveillance camera(s). When the camera detects a subject, it can periodically trigger recalibration and update the radio signal data accordingly. This technique requires camera access occasionally each month, minimizing computational costs and reducing privacy concerns when compared to localization techniques solely based on cameras. Through experiments in an open indoor space, we show that this scheme can retain good localization results while avoiding manual recalibration.

Original languageEnglish
Title of host publicationSenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems
Pages339-340
Number of pages2
DOIs
StatePublished - 2012
Event10th ACM Conference on Embedded Networked Sensor Systems, SenSys 2012 - Toronto, ON, Canada
Duration: Nov 6 2012Nov 9 2012

Publication series

NameSenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems

Conference

Conference10th ACM Conference on Embedded Networked Sensor Systems, SenSys 2012
Country/TerritoryCanada
CityToronto, ON
Period11/6/1211/9/12

Keywords

  • Automatic recalibration
  • Computer vision
  • Device-free passive localization
  • Linear discriminant analysis
  • Rss fingerprint

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