Hot or not? Robust and accurate continuous thermal imaging on FLIR cameras
Loading...
Files
Accepted version
Date
2019-03
Authors
Malmivirta, Titti
Hamberg, Jonatan
Lagerspetz, Eemil
Li, Xin
Peltonen, Ella
Flores, Huber
Nurmi, Petteri
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
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.
Description
Keywords
Thermal sensing , Thermal imaging , Sensor calibration , Deep learning , Mobile computing , Sensing , IoT , Pervasive computing , Internet of Things (IoT)
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
Link to publisher’s version
Collections
Copyright
© 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.