Applying Faster R-CNN in Extremely Low-Resolution Thermal Images for People Detection

In today's cities, it is increasingly normal to see different systems based on Artificial Intelligence (AI) that help citizens and government institutions in their daily lives. This is possible thanks to the Internet of Things (IoT). In this paper we present a solution using low-resolution ther...

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Bibliographic Details
Published in2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT) pp. 1 - 4
Main Authors Jimenez-Bravo, Diego M., Mutombo, Pierre Masala, Braem, Bart, Marquez-Barja, Johann M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2020
Subjects
Convolutional Neural Network
Faster Region-Convolutional Neural Network
Grid Eye
Heating systems
Image sensors
Internet of Things
Low-Resolution Images
Neural networks
People Detection
Sensor systems
Thermal Images
Thermal sensors
Training
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Summary:In today's cities, it is increasingly normal to see different systems based on Artificial Intelligence (AI) that help citizens and government institutions in their daily lives. This is possible thanks to the Internet of Things (IoT). In this paper we present a solution using low-resolution thermal sensors in combination of deep learning to detect people in the images generated by those sensors. To verify whether the deep learning techniques are appropriate for this type of images of such low resolution, we have implement a Faster Region-Convolutional Neural Network. The results obtained are hopeful and undoubtedly encourage to continue improving this research line. With a perception of 72.85% and given the complexity of the problem presented we consider the results obtained to be highly satisfactory and it encourages us to continue improving the work presented in this article.
DOI:10.1109/DS-RT50469.2020.9213609