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Traditional vision approaches face challenges with poor lighting conditions and raise privacy concerns. WiFi-based solutions enable device-free human counting by detecting disruptions in wireless signals caused by human presence. However, methods using received signal strength indicator are unreliable due to physical obstructions, multipath fading, radio interference, and fluctuating access point power. While WiFi channel state information-based systems are more sensitive to environmental changes, they lack standardization, limiting their practicality. To overcome these limitations, this paper presents <jats:italic>Time4Count<\/jats:italic>, an innovative device-free indoor human counting system that leverages round trip time measurements to achieve high accuracy and scalability. <jats:italic>Time4Count<\/jats:italic> capitalizes on human-induced fluctuations in signal propagation time to accurately estimate the number of individuals in a space. By employing a multivariate transformer-based feature extraction method, the system effectively mitigates non-line-of-sight errors and signal distortions, ensuring robust performance even in cluttered indoor environments. Additionally, <jats:italic>Time4Count<\/jats:italic> integrates spatial discretization and multi-label classification techniques, enabling it to count an unlimited number of individuals in real-time. The system was rigorously evaluated in two realistic, cluttered environments using commodity hardware, involving up to 15 participants. Experimental results reveal that <jats:italic>Time4Count<\/jats:italic> achieves an high counting accuracy of 92.7%. To our knowledge, <jats:italic>Time4Count<\/jats:italic> is the first RTT-based indoor counting system, providing a precise solution for indoor monitoring.\u00a0Implementation is available at:\u00a0<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/mclab-osaka\/time4count\" ext-link-type=\"uri\">https:\/\/github.com\/mclab-osaka\/time4count<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s00521-025-11540-8","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T03:46:25Z","timestamp":1756439185000},"page":"23591-23617","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Round trip time meets transformers: high-fidelity human counting in cluttered environments"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8184-3883","authenticated-orcid":false,"given":"Haruki","family":"Yonekura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8278-8801","authenticated-orcid":false,"given":"Hamada","family":"Rizk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2273-4876","authenticated-orcid":false,"given":"Hirozumi","family":"Yamaguchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"11540_CR1","doi-asserted-by":"crossref","unstructured":"Boominathan L, Kruthiventi SSS, Babu RV (2016) Crowdnet: a deep convolutional network for dense crowd counting. 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