Round trip time meets transformers: high-fidelity human counting in cluttered environments

Yonekura, Haruki; Rizk, Hamada; Yamaguchi, Hirozumi

Neural Computing and Applications, 2025, 37(28), 23591-23617

Number of Access:216（2026-04-28 23:48 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/103004

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Title
Round trip time meets transformers: high-fidelity human counting in cluttered environments
Creator
CreatorName
Yonekura, Haruki
NRID
1000030890280 1000030890280
ORCID
0000-0002-8278-8801 0000-0002-8278-8801
researchmap permalink
rizk rizk
Scopus Author ID
57204967686 57204967686
CreatorName
Rizk, Hamada
NRID
1000080314409 1000080314409
ORCID
0000-0003-2273-4876 0000-0003-2273-4876
researchmap permalink
hirozumi.yamaguchi hirozumi.yamaguchi
Scopus Author ID
7404274201 7404274201
CreatorName
Yamaguchi, Hirozumi
Subject
Cluttered environments
Multi-label classification
Multivariate transformer
People counting
Round trip time
Description
The version of record of this article, first published in Neural Comput & Applic 37, 23591 (2025), is available online at Publisher’s website: https://doi.org/10.1007/s00521-025-11540-8
Abstract
Accurate human counting in indoor environments is essential for optimizing people-centric applications, such as crowd management, disaster response, and monitoring in settings like shopping malls and healthcare facilities. 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 Time4Count, an innovative device-free indoor human counting system that leverages round trip time measurements to achieve high accuracy and scalability. Time4Count 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, Time4Count 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 Time4Count achieves an high counting accuracy of 92.7%. To our knowledge, Time4Count is the first RTT-based indoor counting system, providing a precise solution for indoor monitoring. Implementation is available at: https://github.com/mclab-osaka/time4count.
Publisher
Springer Science and Business Media Deutschland GmbH
Source Title
Neural Computing and Applications
Volume (Issue)
37(28)
Page
23591-23617
Date of Issued
2025-08-29
Language
English
Handle URL
https://hdl.handle.net/11094/103004
PISSN
09410643
eISSN
14333058
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1007/s00521-025-11540-8
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article