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

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dc.contributor.author Malmivirta, Titti
dc.contributor.author Hamberg, Jonatan
dc.contributor.author Lagerspetz, Eemil
dc.contributor.author Li, Xin
dc.contributor.author Peltonen, Ella
dc.contributor.author Flores, Huber
dc.contributor.author Nurmi, Petteri
dc.date.accessioned 2019-12-04T15:58:48Z
dc.date.available 2019-12-04T15:58:48Z
dc.date.issued 2019-03
dc.identifier.citation Malmivirta, 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.2019.8767423 en
dc.identifier.startpage 1 en
dc.identifier.endpage 9 en
dc.identifier.isbn 978-1-5386-9148-9
dc.identifier.issn 2474-249X
dc.identifier.issn 2474-2503
dc.identifier.uri http://hdl.handle.net/10468/9328
dc.identifier.doi 10.1109/PERCOM.2019.8767423 en
dc.description.abstract Wearable 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.sponsorship Academy of Finland (grants 317875, 297741, 296139, and 303825, and 6Genesis Flagship (grant 318927)) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en
dc.relation.uri https://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.subject Thermal sensing en
dc.subject Thermal imaging en
dc.subject Sensor calibration en
dc.subject Deep learning en
dc.subject Mobile computing en
dc.subject Sensing en
dc.subject IoT en
dc.subject Pervasive computing en
dc.subject Internet of Things (IoT) en
dc.title Hot or not? Robust and accurate continuous thermal imaging on FLIR cameras en
dc.type Conference item en
dc.internal.authorcontactother Ella Peltonen, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000, Email: ella.peltonen@insight-centre.org en
dc.internal.availability Full text available en
dc.description.version Accepted Version en
dc.contributor.funder Academy of Finland en
dc.description.status Peer reviewed en
dc.internal.conferencelocation Kyoto, Japan en
dc.internal.IRISemailaddress ella.peltonen@insight-centre.org en
dc.relation.project info:eu-repo/grantAgreement/AKA//317875/FI/A Social-aware Utility MarketPlace for Self-organizing Computing at the Edge./ en
dc.relation.project info:eu-repo/grantAgreement/AKA//297741/FI/UbiSpark: Harnessing the Little Big Engines of IoT/ en
dc.relation.project info:eu-repo/grantAgreement/AKA//296139/FI/Sampling in Pervasive Sensing Systems/ en
dc.relation.project info:eu-repo/grantAgreement/AKA//303825/FI/Context Sensing for Security/ en


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