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Title
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Applying faster R-CNN in extremely low-resolution thermal images for people detection
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Proceedings. - Los Alamitos, Calif
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DISTRIBUTED SIMULATION AND REAL TIME APPLICATIONS (DS-RT)
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Source (book)
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IEEE/ACM 24th International Symposium on Distributed Simulation and Real, Time Applications (DS-RT), SEP 14-16, 2020, ELECTR NETWORK
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Publication
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New york
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Ieee
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2020
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ISSN
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1550-6525
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ISBN
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978-1-72817-343-6
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DOI
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10.1109/DS-RT50469.2020.9213609
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Volume/pages
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(2020)
, p. 37-40
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ISI
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000628982300006
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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