University of Oulu

T. Malmivirta et al., "Hot or Not? Robust and Accurate Continuous Thermal Imaging on FLIR cameras," 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom, Kyoto, Japan, 2019, pp. 1-9. doi: 10.1109/PERCOM.2019.8767423

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

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Author: Malmivirta, Titti1; Hamberg, Jonatan1; Lagerspetz, Eemil1;
Organizations: 1Department of Computer Science, University of Helsinki, Helsinki, Finland
2Insight Centre for Data Analytics, University College Cork, Cork, Ireland
3University of Oulu, Oulu, Finland
4Lancaster University, Lancaster, United Kingdom
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202003249098
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-03-24
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.

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Series: IEEE International Conference on Pervasive Computing and Communications
ISSN: 2474-2503
ISSN-E: 2474-249X
ISSN-L: 2474-249X
ISBN: 978-1-5386-9148-9
ISBN Print: 978-1-5386-9149-6
Pages: 1 - 9
DOI: 10.1109/PERCOM.2019.8767423
OADOI: https://oadoi.org/10.1109/PERCOM.2019.8767423
Host publication: 2019 IEEE International Conference on Pervasive Computing and Communications, PerCom 2019, 11-15 March 2019, Kyoto, Japan
Conference: IEEE International Conference on Pervasive Computing and Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
IoT
Funding: This research has been financially supported by Academy of Finland grants 317875, 297741, 296139, and 303825, and 6Genesis Flagship (grant 318927). This article only reflects the authors’ views.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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