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Distance Estimation in Thermal Cameras Using Multi-Task Cascaded Convolutional Neural Network

Authors
Caliwag, Ej Miguel FranciscoCaliwag, AngelaBaek, Bong-KiJo, YongraeChung, HaeLim, Wansu
Issue Date
1-Sep-2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Cameras; Estimation; Faces; Face detection; Temperature measurement; Face recognition; Task analysis; Deep learning; distance estimation; face detection; MTCNN; thermal camera
Citation
IEEE SENSORS JOURNAL, v.21, no.17, pp 18519 - 18525
Pages
7
Journal Title
IEEE SENSORS JOURNAL
Volume
21
Number
17
Start Page
18519
End Page
18525
URI
https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/25808
DOI
10.1109/JSEN.2021.3092382
ISSN
1530-437X
1558-1748
Abstract
The rapid growth of the current pandemic (COVID-19) requires the use of thermal cameras that can perform fast and automatic body temperature measurement. The accuracy of the temperature measurement is affected by its distance from a person. Conventional distance estimation methods utilize the coordinates of the bounding box provided by several face detection algorithms such as YOLOv3 and SSD. The bounding box output of these methods varies which causes inaccurate distance estimation results. In this study, we propose a distance estimation method for thermal camera applications based on the coordinates of the facial key points extracted using multi-task cascaded convolutional neural network. The result obtained in this study proves that the proposed method exhibits higher accuracy (root mean square error of 2.9695 cm in comparison with an RMSE of 25.26 cm using other methods) and the least CPU and memory consumption in comparison with conventional methods.
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