2025-07-03 東京科学大学
＜関連情報＞
- https://www.isct.ac.jp/ja/news/d5eaixm33mis
- https://www.isct.ac.jp/plugins/cms/component_download_file.php?type=2&pageId=&contentsId=1&contentsDataId=1840&prevId=&key=ad23e9c78ccc8f6154a9c266a535cd9b.pdf
- https://ieeexplore.ieee.org/document/10999356
5Gミリ波信号のビームレベルRSRPを利用した屋内パッシブ群集計数 Leveraging Beam-Level RSRP of 5G mmWave Signal for Indoor Passive Crowd Counting
Nopphon Keerativoranan; Hang Song; Hsueh Han Wu; Saurabh Verma; Daisuke Ichihashi; Kelvin Cheng;…
2025 19th European Conference on Antennas and Propagation (EuCAP) Date Added to IEEE Xplore: 21 May 2025
DOI:https://doi.org/10.23919/EuCAP63536.2025.10999356
Abstract
Crowd counting estimation are crucial for applications such as intrusion detection, event crowd control, and retail staffing optimization. Traditional visual-based systems, while effective, are limited by challenges in low-light conditions, privacy concerns, and restricted field-of-view. RF-based approaches offer a non-intrusive alternative but struggle with defining coverage zones and update rates. This work explores the use of 5G mmWave signal to address these issues, leveraging beam-level reference signal received power (RSRP) for crowd monitoring. Using a fingerprinting approach and K-nearest neighbor classifier with additional beam-level RSRP process, this work comprehensively evaluates the crowd counting performance with the crowd up to five walking individuals in indoor office. The results demonstrated the feasibility of using commercial 5G mobile communication system for passive indoor crowd counting.
