Hot or not? Robust and accurate continuous thermal imaging on FLIR cameras

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dc.contributor.authorMalmivirta, Titti
dc.contributor.authorHamberg, Jonatan
dc.contributor.authorLagerspetz, Eemil
dc.contributor.authorLi, Xin
dc.contributor.authorPeltonen, Ella
dc.contributor.authorFlores, Huber
dc.contributor.authorNurmi, Petteri
dc.contributor.funderAcademy of Finlanden
dc.date.accessioned2019-12-04T15:58:48Z
dc.date.available2019-12-04T15:58:48Z
dc.date.issued2019-03
dc.description.abstractWearable thermal imaging is emerging as a powerful and increasingly affordable sensing technology. Current thermal imaging solutions are mostly based on uncooled forward looking infrared (FLIR), which is susceptible to errors resulting from warming of the camera and the device casing it. To mitigate these errors, a blackbody calibration technique where a shutter whose thermal parameters are known is periodically used to calibrate the measurements. This technique, however, is only accurate when the shutter's temperature remains constant over time, which rarely is the case. In this paper, we contribute by developing a novel deep learning based calibration technique that uses battery temperature measurements to learn a model that allows adapting to changes in the internal thermal calibration parameters. Our method is particularly effective in continuous sensing where the device casing the camera is prone to heating. We demonstrate the effectiveness of our technique through controlled benchmark experiments which show significant improvements in thermal monitoring accuracy and robustness.en
dc.description.sponsorshipAcademy of Finland (grants 317875, 297741, 296139, and 303825, and 6Genesis Flagship (grant 318927))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMalmivirta, T., Hamberg, J., Lagerspetz, E., Li, X., Peltonen, E., Flores, H. and Nurmi, P. (2019) 'Hot or Not? Robust and Accurate Continuous Thermal Imaging on FLIR cameras', 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom), Kyoto, Japan, 11-15 March 2019, 1-9. doi: 10.1109/PERCOM.2019.8767423en
dc.identifier.doi10.1109/PERCOM.2019.8767423en
dc.identifier.endpage9en
dc.identifier.isbn978-1-5386-9148-9
dc.identifier.issn2474-249X
dc.identifier.issn2474-2503
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/9328
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/AKA//317875/FI/A Social-aware Utility MarketPlace for Self-organizing Computing at the Edge./en
dc.relation.projectinfo:eu-repo/grantAgreement/AKA//297741/FI/UbiSpark: Harnessing the Little Big Engines of IoT/en
dc.relation.projectinfo:eu-repo/grantAgreement/AKA//296139/FI/Sampling in Pervasive Sensing Systems/en
dc.relation.projectinfo:eu-repo/grantAgreement/AKA//303825/FI/Context Sensing for Security/en
dc.relation.urihttps://ieeexplore.ieee.org/document/8767423
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectThermal sensingen
dc.subjectThermal imagingen
dc.subjectSensor calibrationen
dc.subjectDeep learningen
dc.subjectMobile computingen
dc.subjectSensingen
dc.subjectIoTen
dc.subjectPervasive computingen
dc.subjectInternet of Things (IoT)en
dc.titleHot or not? Robust and accurate continuous thermal imaging on FLIR camerasen
dc.typeConference itemen
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