{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:25:30Z","timestamp":1781022330262,"version":"3.54.1"},"reference-count":70,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 \u00d7 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 \u00d7 12 and 8 \u00d7 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 \u00d7 12. For frames of size 8 \u00d7 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.<\/jats:p>","DOI":"10.3390\/info13030132","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:38:16Z","timestamp":1646599096000},"page":"132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Low-Resolution Infrared Array Sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7055-9318","authenticated-orcid":false,"given":"Mondher","family":"Bouazizi","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2555-1998","authenticated-orcid":false,"given":"Chen","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-1426","authenticated-orcid":false,"given":"Tomoaki","family":"Ohtsuki","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103179","DOI":"10.1016\/j.jnca.2021.103179","article-title":"Internet of Healthcare Things: A contemporary survey","volume":"192","author":"Ketu","year":"2021","journal-title":"J. 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