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<p>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 <i>Time4Count</i>, an innovative device-free indoor human counting system that leverages round trip time measurements to achieve high accuracy and scalability. <i>Time4Count</i> 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, <i>Time4Count</i> 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 <i>Time4Count</i> achieves an high counting accuracy of 92.7%. To our knowledge, <i>Time4Count</i> is the first RTT-based indoor counting system, providing a precise solution for indoor monitoring.&#xa0;Implementation is available at:&#xa0;<a href="https://github.com/mclab-osaka/time4count">https://github.com/mclab-osaka/time4count</a>.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月29日

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  • Haruki Yonekura,
  • Hamada Rizk,
  • Hirozumi Yamaguchi

摘要

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.