Crowd counting with WiFi sensing based on iterative attentional feature fusion

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications Pub Date : 2025-06-10 DOI:10.1016/j.comcom.2025.108245

BeiMing Yan, Yong Li, LiMeng Dong, ZeRong Ren, HuiMin Liu, Xiang Gao, Wei Cheng

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引用次数: 0

Abstract

—Crowd counting has great appeal for a variety of applications, such as public transportation, disaster management and building automation. Recently, WiFi-based crowd counting has gained dominance due to its ubiquitous and non-invasive advantages. However, current WiFi-based crowd counting systems have a limitation in that they do not consider the effect of dynamic crowds and static crowds on crowd counting. In contrast to previous studies, this paper investigates the effect of crowds in different states on crowd counting performance, and proposes a WiFi-based multi-state crowd counting system, which can not only count dynamic or static crowds, but also count joint dynamic and static crowds. By analyzing the effect of crowd states on the signal, we demonstrate that the channel state information (CSI) subcarrier distribution can indicate the count of crowds in different states. To this end, we adopt an iterative attentional feature fusion (IAFF) which allows for the fusion of amplitude and phase information from multiple antennas and adaptively assigns weights to amplitude and phase on multiple subcarriers, thus enabling the counting of crowds in various states. The experimental results show that the system has recognition accuracy of 99.38 % for static crowds, 95.94 % for dynamic crowds, and 97.57 % for joint dynamic and static crowds.

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基于迭代注意力特征融合的WiFi感知人群计数

-人群计数在公共交通、灾害管理和楼宇自动化等各种应用中具有很大的吸引力。最近，基于wifi的人群统计以其无所不在和非侵入性的优势占据了主导地位。然而，目前基于wifi的人群计数系统存在一个局限性，即没有考虑动态人群和静态人群对人群计数的影响。在前人研究的基础上，本文研究了不同状态的人群对人群计数性能的影响，提出了一种基于wifi的多状态人群计数系统，该系统不仅可以对动态或静态人群进行计数，还可以对联合动态和静态人群进行计数。通过分析人群状态对信号的影响，证明了信道状态信息（CSI）子载波分布可以反映不同状态下人群的数量。为此，我们采用迭代注意特征融合（IAFF），该融合允许来自多个天线的幅度和相位信息融合，并自适应地为多个子载波的幅度和相位分配权重，从而实现不同状态下的人群计数。实验结果表明，该系统对静态人群的识别准确率为99.38%，对动态人群的识别准确率为95.94%，对动静联合人群的识别准确率为97.57%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Communications 工程技术-电信学

CiteScore

14.10

自引率

5.00%

发文量

397

审稿时长

66 days

期刊介绍： Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today''s computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms. Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications.