Computer Science ›› 2019, Vol. 46 ›› Issue (11): 297-303.doi: 10.11896/jsjkx.191100506C

• Interdiscipline & Frontier • Previous Articles     Next Articles

WiCount:A Crowd Counting Method Based on WiFi Channel State Information

DING Ya-san, GUO Bin, XIN Tong, WANG Pei, WANG Zhu, YU Zhi-wen   

  1. (School of Computer Science,Northwestern Polytechnical University,Xi’an 710072,China)
  • Received:2018-10-07 Online:2019-11-15 Published:2019-11-14

Abstract: Crowd counting is the process of monitoring the number of people in a certain area,which is crucial in traffic supervision,etc.For example,counting people waiting in lines at airports or retail stores could be used for improving the service.At present,some methods based on videos (or images) and wearable devices have been proposed,but there are some shortcomings in these schemes.For example,the camera can only monitor within the range of sight distance,and wearable devices need people to wear them consciously.Some scholars have made use of radar related technology torealize the number,but its cost is very high.In this paper,an indoor crowd counting scheme,WiCount,based on WiFi signals was proposed.WiCount aims at a fine-grained indoor people counting scheme,which can accurately identify the number of people at different positions.According to the relationship between the number of indoor people and the amplitudes fluctuation of CSI,features are extracted,which are contributed to mitigate the difference of CSI data produced by the same number of people in distinct positions,and then three classifiers (SVM,KNN,BP Neural Network) are trained to identify the number of people in the monitoring area.Prototype systems is implemented in a laboratory and a meeting room respectively,and the recognition is fine when the number of people is on the small side.In the laboratory,the accuracy is up to 90% in the case of no more than 4 persons.In the meeting room,the results show that no matter where people move,the accuracy can reach 89.58% in the case of no more than 2 persons.

Key words: Channel state information, Crowd counting, Machine learning, WiFi sensing, Wireless sensing

CLC Number: 

  • TP399
[1]WEPPNER J,LUKOWICZ P.Collaborative Crowd Density Estimation with Mobile Phones[C]∥2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).IEEE,2012:193-200.
[2]LI M,ZHANG Z,HUANG K,et al.Estimating the number ofpeople in crowded scenes by MID based foreground segmentation and head-shoulder detection[C]∥International Conference on Pattern Recognition.IEEE,2009:1-4.
[3]CHAN A B,LIANG Z S J,VASCONCELOS N.Privacy preserving crowd monitoring:Counting people without people models or tracking[C]∥IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2008).IEEE,2008:1-7.
[4]IDREES H,SALEEMI I,SEIBERT C,et al.Multi-sourceMulti-scale Counting in Extremely Dense Crowd Images[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2013:2547-2554.
[5]SUBBURAMAN V B,DESCAMPS A,CARIN-COTTE C.Counting People in the Crowd Using a Generic Head Detector[C]∥IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.IEEE,2012:470-475.
[6]LIYANAGE M,CHANG C,SRIRAMA S N.mePaaS:Mobile-Embedded Platform as a Service for Distributing Fog Computing to Edge Nodes[C]∥International Conference on Parallel and Distributed Computing,Applications and Technologies.IEEE,2017:73-80.
[7]KANNAN P G,VENKATAGIRI S P,CHAN M C,et al.Lowcost crowd counting using audio tones[C]∥ACM Conference on Embedded Network Sensor Systems.ACM,2012:155-168.
[8]VERSICHELE M,NEUTENS T,DELAFONT-AINE M,et al.The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events:a case study of the Ghent Festivities[J].Applied Geography,2012,32(2):208-220.
[9]WEPPNER J,LUKOWICZ P.Bluetooth based CollaborativeCrowd Density Estimation with Mobil-e Phones[C]∥IEEE International Conference on Pervasive Computing and Communications (PerCom).IEEE,2013:193-200.
[10]NAKATSUKA M,IWATANI H,KATTO J.A study on passive crowd density estimation using wireless sensors[C]∥The 4th Intl.Conf.on Mobile Computing and Ubiquitous Networking (ICMU 2008).IEEE,2008:54-59.
[11]SCHAUER L,WERNER M,MARCUS P.Es-timating Crowd Densities and Pedestrian Flows Using Wi-Fi and Bluetooth[C]∥International Conference on Mobile and Ubiquitous Systems:Computing,Networking and Services.ACM,2014:171-177.
[12]DING H,HAN J,LIU A X,et al.Human object estimation via backscattered radio frequency signal[C]∥Computer Communications.IEEE,2015:1652-1660.
[13]YUAN Y,ZHAO J,QIU C,et al.Estimating Crowd Density in an RF-Based Dynamic Environment[J].IEEE Sensors Journal,2013,13(10):3837-3845.
[14]FADHLULLAH S Y,ISMAIL W.A Statistical Approach inDesigning an RF-Based Human Crowd Density Estimation System[J].International Journal of Distributed Sensor Networks,2016,12:1-9.
[15]MOHAN L,CHII C,SATISH N S,et al.Indoor People Density Sensing using Wi-Fi and Channel State Information[C]∥ACM 14th EAI International Conference on Mobile and Ubiquitous Systems:Computing,Networking and Services.ACM,2017:37-47.
[16]DOMENICO S D,SANCTIS M D,CIANCA E,et al.A trainedonce crowd counting method using differential WiFi channel state information[C]∥Proceedings of the 3rd International on Workshop on Physical Analytics.ACM,2016:37-42.
[17]XI W,ZHAO J,LI X Y,et al.Electronic frog eye:Countingcrowd using WiFi[C]∥IEEE INFOCOM 2014-IEEE Confe-rence on Computer Communications.IEEE,2014:361-369.
[18]YOSHIDA T,TANIGU-CHI Y.Estimating the number of people using existing WiFi access point in indoor environment[C]∥Proceedings of the 6th European Conference of Computer Science (ECCS’15).2015:46-53.
[19]YANG Z,ZHOU Z,LIU Y.From RSSI to CSI:Indoor localization via channel response[J].ACM Computing Surveys(CSUR),2013,46(2):25-25.
[20]HALPERIN 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.
[21]SEN S,LEE J,KIM K H,et al.Avoiding multipath to revive inbuilding Wi-Fi localization[C]∥Proceeding of the 11th Annual International Conference on Mobile Systems,Applications,and Services.ACM,2013:249-262.
[22]MOHAMMADMORADI H,YIN S,GNAWA-LI O.Room Occupancy Estimation Through WiFi,UWB,and Light Sensors Mounted on Doorways[C]∥Proceedings of the 2017 International Conference on Smart Digital Environment.ACM,2017:27-34.
[23]CHENG Y K,CHANG R Y.Device-Free Indoor People Counting Using Wi-Fi Channel State Information for Internet of Things[C]∥2017 IEEE Global Communications Conference(GLOBECOM 2017).IEEE,2017:1-6.
[24]GONG L,YANG W,ZHOU Z,et al.An adaptive wireless passive human detection via fine-grained physical layer information[J].Ad Hoc Networks,2016,38(C):38-50.
[25]HALPERIN 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.
[1] LENG Dian-dian, DU Peng, CHEN Jian-ting, XIANG Yang. Automated Container Terminal Oriented Travel Time Estimation of AGV [J]. Computer Science, 2022, 49(9): 208-214.
[2] NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296.
[3] HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11.
[4] LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network [J]. Computer Science, 2022, 49(8): 257-266.
[5] ZHANG Guang-hua, GAO Tian-jiao, CHEN Zhen-guo, YU Nai-wen. Study on Malware Classification Based on N-Gram Static Analysis Technology [J]. Computer Science, 2022, 49(8): 336-343.
[6] CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323.
[7] XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning [J]. Computer Science, 2022, 49(6A): 397-406.
[8] YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515.
[9] QING Chao-jin, DU Yan-hong, YE Qing, YANG Na, ZHANG Min-tao. Enhanced ELM-based Superimposed CSI Feedback Method with CSI Estimation Errors [J]. Computer Science, 2022, 49(6A): 632-638.
[10] LI Ya-ru, ZHANG Yu-lai, WANG Jia-chen. Survey on Bayesian Optimization Methods for Hyper-parameter Tuning [J]. Computer Science, 2022, 49(6A): 86-92.
[11] ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms [J]. Computer Science, 2022, 49(6A): 93-99.
[12] WANG Fei, HUANG Tao, YANG Ye. Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion [J]. Computer Science, 2022, 49(6A): 784-789.
[13] XU Jie, ZHU Yu-kun, XING Chun-xiao. Application of Machine Learning in Financial Asset Pricing:A Review [J]. Computer Science, 2022, 49(6): 276-286.
[14] YAO Xiao-ming, DING Shi-chang, ZHAO Tao, HUANG Hong, LUO Jar-der, FU Xiao-ming. Big Data-driven Based Socioeconomic Status Analysis:A Survey [J]. Computer Science, 2022, 49(4): 80-87.
[15] LI Ye, CHEN Song-can. Physics-informed Neural Networks:Recent Advances and Prospects [J]. Computer Science, 2022, 49(4): 254-262.
Full text



No Suggested Reading articles found!