{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:24:23Z","timestamp":1773098663954,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea government (MSIT)","award":["No. RS-2023-00212780"],"award-info":[{"award-number":["No. RS-2023-00212780"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Microcontrollers (MCUs) have been deployed on numerous IoT devices due to their compact sizes and low costs. MCUs are capable of capturing sensor data and processing them. However, due to their low computational power, applications processing sensor data with deep neural networks (DNNs) have been limited. In this paper, we propose MiCrowd, a floating population measurement system with a tiny DNNs running on MCUs since the data have essential value in urban planning and business. Moreover, MiCrowd addresses the following important challenges: (1) privacy issues, (2) communication costs, and (3) extreme resource constraints on MCUs. To tackle those challenges, we designed a lightweight crowd-counting deep neural network, named MiCrowdNet, which enables on-MCU inferences. In addition, our dataset is carefully chosen and completely re-labeled to train MiCrowdNet for counting people from an mobility view. Experiments show the effectiveness of MiCrowdNet and our relabeled dataset for accurate on-device crowd counting.<\/jats:p>","DOI":"10.3390\/s23073586","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T01:31:30Z","timestamp":1680139890000},"page":"3586","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MiCrowd: Vision-Based Deep Crowd Counting on MCU"],"prefix":"10.3390","volume":"23","author":[{"given":"Sungwook","family":"Son","sequence":"first","affiliation":[{"name":"Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2348-0206","authenticated-orcid":false,"given":"Ahreum","family":"Seo","sequence":"additional","affiliation":[{"name":"Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gyeongseon","family":"Eo","sequence":"additional","affiliation":[{"name":"Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwangyeon","family":"Gill","sequence":"additional","affiliation":[{"name":"Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taesik","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8605-5077","authenticated-orcid":false,"given":"Hyung-Sin","family":"Kim","sequence":"additional","affiliation":[{"name":"Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"ref_1","unstructured":"(2023, February 21). 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