Computer Science ›› 2025, Vol. 52 ›› Issue (10): 317-327.doi: 10.11896/jsjkx.240800060

• Computer Network • Previous Articles     Next Articles

WiLCount:A Lightweight Crowd Counting Model for Wireless Perception Scenarios

DUAN Pengsong, ZHANG Yihang, FANG Tao, CAO Yangjie, WANG Chao   

  1. School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450003,China
  • Received:2024-08-12 Revised:2024-10-15 Online:2025-10-15 Published:2025-10-14
  • About author:DUAN Pengsong,born in 1983,Ph.D,associate professor,is a member of CCF(No.43410M).His main research interests include wireless sensing,IoT and machine learning.
    CAO Yangjie,born in 1976,Ph.D,professor,is a member of CCF(No.17620S).His main research interests include machine learning,computer vision and high-performance computing.
  • Supported by:
    Zhengzhou Collaborative Innovation Major Project(20XTZX06013),Natural Science Foundation of Henan Pro-vince(222300420295),China Engineering Science and Technology Development Strategy Henan Research Institute Strategic Consulting Research Project(2022HENYB03) and Henan Province Science and Technology Research Project(232102210050).

Abstract: To address the challenges of limited accuracy and high computational complexity in crowd counting models due to the absence of spatial features in CSI,this paper proposes a lightweight model,WiLCount,based on amplitude-phase fusion.Firstly,a linear transformation method is applied to calibrate the phase data,addressing the issues of carrier frequency offset and sampling frequency offset in the raw phase information,which would otherwise render it unusable.Next,the amplitude-phase data is reconstructed into a two-dimensional image to fully exploit the spatial mapping features of crowd count inherent in CSI data.Finally,WiLCount is developed by integrating depthwise separable convolutions with a multi-branch structure.Due to the lack of publicly available datasets in the Wi-Fi-based crowd counting field,a self-collected dataset,leading the industry in terms of crowd scale and activity diversity,is meticulously constructed and released.Experimental results demonstrate that WiLCount achieves a recognition accuracy of up to 99.58% on the self-collected dataset,with a parameter size of only 4% of that of comparable mo-dels.Significant improvements over existing methods have been observed,with the model exhibiting strong robustness.

Key words: Wi-Fi sensing,Channel state information,Crowd counting,Amplitude-phase fusion,Depthwise separable convolution

CLC Number: 

  • TP393
[1]CHOI J W,QUAN X,CHO S H.Bi-Directional Passing People Counting System based on IR-UWB Radar Sensors[J].IEEE Internet of Things Journal,2017,5(2):512-522.
[2]LIU P C,NGUANG S K,PARTRIDGE A.Occupancy Inference Using Pyroelectric Infrared Sensors Through Hidden Markov Models[J].IEEE Sensors Journal,2016,16(4):1062-1068.
[3]ZHANG Y Y,ZHOU D S,CHEN S Q,et al.Single-ImageCrowd Counting via Multi-Column Convolutional Neural Network[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2016:589-597.
[4]LIN H,MA Z H,JI R,et al.Boosting Crowd Counting via Multifaceted Attention[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.IEEE,2022:19596-19605.
[5]LU Y,LYU S H,WANG X D,et al.A survey onWiFi based human behavior analysis technology[J].Chinese Journal of Computers,2019,42(2):231-251.
[6]HAN Z J,LU Z M,WEN X M,et al.CentiTrack:Toward Centimeter-Level Passive Gesture Tracking With Commodity WiFi[J].IEEE Internet of Things Journal,2023,10(14):13012-13027.
[7]ZHANG C S,JIAO W G.ImgFi:A High Accuracy and Lightweight Human Activity Recognition Framework Using CSI Image[J].IEEE Sensors Journal,2023,23(18):21966-21977.
[8]LIU K Z,PEI D S,ZHANG S K,et al.WiCrew:Gait-Based Crew Identification for Cruise Ships Using Commodity WiFi[J].IEEE Internet of Things Journal,2023,10(8):6960-6972.
[9]TEWES S,HEINRICHS M,KRONBERGER R,et al.IRS-Enabled Breath Tracking With Colocated Commodity WiFi Transceivers[J].IEEE Internet of Things Journal,2023,10(8):6870-6886.
[10]JIN R N,ZHOU J Y,HU J H,et al.Toward Practical Lightweight Passive Human Tracking Using WiFi Sensing[J].IEEE Internet of Things Journal,2023,10(15):13769-13783.
[11]BAHL P,PADMANABHAN V N.RADAR:an in-building RF-based user location and tracking system[C]//Proceedings IEEE INFOCOM 2000.Conference on Computer Communications.Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies.IEEE,2000:775-784.
[12]NAKATSUKA M,IWATANI H,KATTO J.A study on pas-sive crowd density estimation using wireless sensors[C]//In The 4th Intl.Conf.on Mobile Computing and Ubiquitous Networking.2008.
[13]YOSHIDA T,TANIGUCHI Y.Estimating the number of people using existing WiFi access point in indoor environment[C]//International Conference on Evolutionary Computing;European Conference of Computer Science.IEEE,2015.
[14]DEPATLA S,MURALIDHARAN A,MOSTOFI Y.Occupancy Estimation Using Only WiFi Power Measurements[J].IEEE Journal on Selected Areas in Communications,2015,33(7):1381-1393.
[15]LI H C,CHAN E,GUO X N,et al.Wi-Counter:Smartphone-Based People Counter Using Crowdsourced Wi-Fi Signal Data[J].IEEE Transactions on Human-Machine Systems,2015,45(4):442-452.
[16]KURA S,YAMAGUCHI H,SHIRAISHI Y.Low-cost Pedestrian Counter Using Wi-Fi APs for Smart Building Applications[C]//2018 IEEE 42nd Annual Computer Software and Applications Conference.IEEE,2018:640-645.
[17]HALPERIRR D,HU W,SHETH A,et al.Tool release:gathering 802.11n traces with channel state information[J].ACM Sigcomm Computer Communication Review,2011,41(1):53-53.
[18]YANG Z,ZHOU Z M,LIU Y H.From RSSI to CSI:Indoor Localization via Channel Response[J].ACM Computing Surveys,2013,46(2):1-32.
[19]XI W,ZHAO J Z,LI X Y,et al.Electronic frog eye:Counting crowd using WiFi[C]//IEEE INFOCOM 2014-IEEE Confe-rence on Computer Communications.IEEE,2014:361-369.
[20]DING Y S,GUO B,XIN T,et al.WiCount:A Crowd Counting Method Based on WiFi Channel State Information[J].Computer Science,2019,46(11):297-303.
[21]WANG S N,XUN Y J,ZHAO J,et al.A Novel PersonnelCounting Method Based on WiFi Perception[C]//2022 IEEE 23rd International Conference on High Performance Switching and Routing.IEEE,2022:206-211.
[22]CHOI H,FUJIMOTO M,MATSUI T,et al.Wi-CaL:WiFi Sensing and Machine Learning based Device-Free Crowd Coun-ting and Localization[J].IEEE Access,2022,10:24395-24410.
[23]GUO Z X,FU X,SHENG B Y,et al.TWCC:A RobustThrough-the-Wall Crowd Counting System Using Ambient WiFi Signals[J].IEEE Transactions on Vehicular Technology,2022,71(4):4198-4211.
[24]HOU H W,BI S Z,ZHENG L L,et al.DASECount:Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-Shot Learning[J].IEEE Internet of Things Journal,2022,10(8):7038-7050.
[25]KHAN D,HO I.CrossCount:Efficient Device-Free CrowdCounting by Leveraging Transfer Learning[J].IEEE Internet of Things Journal,2023,10(5):4049-4058.
[26]YOUSSEF M,MAH M,AGRAWALA A.Challenges:Device-free passive localization for wireless environments[C]//ACM/IEEE International Conference on Mobile Computing and Networking.ACM,2007:9-14.
[27]BAO N,DU J J,WU C Y,et al.Wi-Breath:A WiFi-Based Contactless and Real-Time Respiration Monitoring Scheme for Remote Healthcare[J].IEEE Journal of Biomedical and Health Informatics,2022,27(5):2276-2285.
[28]SEN S,RADUNOVIC B,CHOUDHURY R R,et al.You are facing the Mona Lisa:spot localization using PHY layer information[C]//ACM SIGMOBILE International Conference on Mobile Systems,Applications,and Services.ACM,2012:183-196.
[29]QIAN K,WU C S,YANG Z,et al.PADS:Passive detection of moving targets with dynamic speed using PHY layer information[C]//2014 20th IEEE International Conference on Parallel and Distributed Systems.IEEE,2014:1-8.
[30]LIU Z J,WANG L,LIU W Y,et al.Human Movement Detection and Gait Periodicity Analysis Using Channel State Information[C]//International Conference on Mobile Ad-hoc & Sensor Networks.IEEE,2016:167-174.
[31]HOWARD A G,ZHU M L,CHEN B,et al.MobileNets:Effi-cient Convolutional Neural Networks for Mobile Vision Applications[J].arXiv:1704.04861,2017.
[32]XIE Y X,LI Z J,LI M.Precise Power Delay Profiling with Commodity Wi-Fi[J].IEEE Transactions on Mobile Computing,2015,18:1342-1355.
[33]GRINGOLI F,SCHULZ M,LINK J,et al.Free Your CSI:A Channel State Information Extraction Platform For Modern Wi-Fi Chipsets[C]//13th ACM International Workshop on Wireless Network Testbeds,Experimental Evaluation and Characterization.ACM,2019:21-28.
[34]HUSSEIN K,CLAUDIO P,FEDERICO P,et al.Impact of Wi-Fi traffic on the IEEE 802.15.4 channels occupation in indoor environments[C]//2009 International Conference on Electromagnetics in Advanced Applications.IEEE,2009:1042-1045.
[35]SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the Inception Architecture for Computer Vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2016:2818-2826.
[36]HE K M,ZHANG X,REN S Q,et al.Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2015:770-778.
[37]DOSOVITSKIY A,BEYER L,KOLESNIKOV A,et al.AnImage is Worth 16×16 Words:Transformers for Image Recognition at Scale [J].arXiv:2010.11929,2020.
[38]CHEN H T,WANG Y H,GUO J Y,et al.VanillaNet:thePower of Minimalism in Deep Learning[J].arXiv:2305.12972,2023.
[39]SANDLER M,HOWARD A,ZHU M L,et al.MobileNetV2:Inverted Residuals and Linear Bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.IEEE,2018:4510-4520.
[40]IANDOLA F N,MOSKEWICZ M W,ASHRAF K,et al.SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and <1 MB model size[J].arXiv:1602.07360,2016.
[41]CAO C Y,YANG X D,DUAN P S.WiDoor:Close-range Contactless Human Identification Approach[J].Cpmputer Science,2023,50(4):388-396.
[1] HU Yongqing, YANG Han, LIU Ziyuan, QING Guangjun, DAI Qinglong. ACCF:Time Prediction Mechanism-driven Top-k Flow Measurement [J]. Computer Science, 2025, 52(10): 98-105.
[2] WANG Pengrui, HU Yuxiang, CUI Pengshuai, DONG Yongji, XIA Jiqiang. SRv6 Functional Conformance Verification Mechanism Based on the Programmable Data Plane [J]. Computer Science, 2025, 52(10): 328-335.
[3] XU Jia, LIU Jingyi, XU Lijie, LIU Linfeng. Wireless Charging Scheduling with Minimized Maximum Return-to-Work Time for Heterogeneous Mobile Rechargeable Devices [J]. Computer Science, 2025, 52(10): 336-347.
[4] WU Moxun, PENG Zeshun, YU Minghe, LI Xiaohua, DONG Xiaomei, NIE Tiezheng, YU Ge. Approach for Lightweight Verifiable Data Management Based on Blockchains [J]. Computer Science, 2025, 52(10): 348-356.
[5] HE Hao, ZHANG Hui. Intrusion Detection Method Based on Improved Active Learning [J]. Computer Science, 2025, 52(10): 357-365.
[6] ZHU Ziyi, ZHANG Jianhui, ZENG Junjieand ZHANG Hongyuan. Security-aware Service Function Chain Deployment Method Based on Deep ReinforcementLearning [J]. Computer Science, 2025, 52(10): 404-411.
[7] WU Jiagao, YI Jing, ZHOU Zehui, LIU Linfeng. Personalized Federated Learning Framework for Long-tailed Heterogeneous Data [J]. Computer Science, 2025, 52(9): 232-240.
[8] SHEN Tao, ZHANG Xiuzai, XU Dai. Improved RT-DETR Algorithm for Small Object Detection in Remote Sensing Images [J]. Computer Science, 2025, 52(8): 214-221.
[9] LONG Tie, XIAO Fu, FAN Weibei, HE Xin, WANG Junchang. Cubic+:Enhanced Cubic Congestion Control for Cross-datacenter Networks [J]. Computer Science, 2025, 52(8): 335-342.
[10] YE Miao, WANG Jue, JIANG Qiuxiang, WANG Yong. SDN-based Integrated Communication and Storage Edge In-network Storage Node Selection Method [J]. Computer Science, 2025, 52(8): 343-353.
[11] FAN Xinggang, JIANG Xinyang, GU Wenting, XU Juntao, YANG Youdong, LI Qiang. Effective Task Offloading Strategy Based on Heterogeneous Nodes [J]. Computer Science, 2025, 52(8): 354-362.
[12] ZHAO Jihong, MA Jian, LI Qianwen, NING Lijuan. Service Function Chain Deployment Method Based on VNF Divided Backup Mechanisms [J]. Computer Science, 2025, 52(7): 287-294.
[13] LIU Wenfei, LIU Jiafei, WANG Qi, WU Jingli, LI Gaoshi. Component Reliability Analysis of Interconnected Networks Based on Star Graph [J]. Computer Science, 2025, 52(7): 295-306.
[14] CHEN Shangyu, HU Hongchao, ZHANG Shuai, ZHOU Dacheng, YANG Xiaohan. Tor Multipath Selection Based on Threaten Awareness [J]. Computer Science, 2025, 52(7): 363-371.
[15] ZHOU Lei, SHI Huaifeng, YANG Kai, WANG Rui, LIU Chaofan. Intelligent Prediction of Network Traffic Based on Large Language Model [J]. Computer Science, 2025, 52(6A): 241100058-7.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!