Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Networks
Published in: Wireless Networks 5/2020

14-02-2020

Device-free crowd counting with WiFi channel state information and deep neural networks

Authors: Rui Zhou, Xiang Lu, Yang Fu, Mingjie Tang

Published in: Wireless Networks | Issue 5/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Crowd counting is of great importance to many applications. Conventional vision-based approaches require line of sight and pose privacy concerns, while most radio-based approaches involve high deployment cost. In this paper, we propose to utilize WiFi channel state information (CSI) to infer crowd count in a device-free way, with only one pair of WiFi transmitter and receiver. The proposed method establishes the statistical relationship between the variation of CSI and the number of people with deep neural networks (DNN) and thereafter estimates the people count according to the real-time CSI through the trained DNN model. Evaluations demonstrate the effectiveness of the method. For the crowd size of 6, the counting error was within 1 person for 100% of the cases. For the crowd size of 34, the counting error was within 1 person for 97.7% of the cases and within 2 persons for 99.3% of the cases.
previous article A multi-tier based clustering framework for scalable and energy efficient WSN-assisted IoT network
next article Prediction of energy consumption for LoRa based wireless sensors network

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Li, M., Zhang, Z., Huang, K., & Tan, T. (2008). Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection. In 2008 19th international conference on pattern recognition (pp. 1–4).
2.
Kim, M., Kim, W., & Kim, C. (2011). Estimating the number of people in crowded scenes. Proceedings of SPIE, 7882(23), 78 820L–78 820L-8.
3.
Kannan, P. G., Venkatagiri, S. P., Chan, M. C., Ananda, A.L., & Peh, L.-S. (2012). Low cost crowd counting using audio tones. In Proceedings of the 10th ACM conference on embedded network sensor systems (SenSys’12) (pp. 155–168). ACM.
4.
Weppner, J., & Lukowicz, P. (2013). Bluetooth based collaborative crowd density estimation with mobile phones. In 2013 IEEE international conference on pervasive computing and communications (PerCom) (pp. 193–200).
5.
Yuan, Y., Zhao, J., Qiu, C., & Xi, W. (2013). Estimating crowd density in an RF-based dynamic environment. IEEE Sensors Journal, 13(10), 3837–3845.CrossRef
6.
Doong, S. H. (2016). Spectral human flow counting with RSSI in wireless sensor networks. In 2016 international conference on distributed computing in sensor systems (DCOSS) (pp. 110–112).
7.
Xu, C., Firner, B., Moore, R. S., Zhang, Y., Trappe, W., Howard, R., Zhang, F., & An, N. (2013). SCPL: Indoor device-free multi-subject counting and localization using radio signal strength. In 2013 ACM/IEEE IPSN (pp. 79–90).
8.
Lv, H., Liu, M., Jiao, T., Zhang, Y., Yu, X., Li, S., Jing, X., & Wang, J. (2013). Multi-target human sensing via UWB bio-radar based on multiple antennas. In TENCON 2013 (pp. 1–4).
9.
He, J., & Arora, A. (2014). A regression-based radar-mote system for people counting. In 2014 PerCom (pp. 95–102).
10.
Cianca, E., Sanctis, M. D., & Domenico, S. D. (2017). Radios as sensors. IEEE Internet of Things Journal, 4(2), 363–373.CrossRef
11.
Nakatsuka, M., Iwatani, H., & Katto, J. (2008). A study on passive crowd density estimation using wireless sensors. In 2008 international conference on mobile computing and ubiquitous networking
12.
Depatla, S., Muralidharan, A., & Mostofi, Y. (2015). Occupancy estimation using only wifi power measurements. IEEE Journal on Selected Areas in Communications, 33(7), 1381–1393.CrossRef
13.
Abdel-Nasser, H., Samir, R., Sabek, I., & Youssef, M. (2013). MonoPHY: Mono-stream-based device-free WLAN localization via physical layer information. In 2013 IEEE WCNC (pp. 4546–4551).
14.
Wu, K., Xiao, J., Yi, Y., Chen, D., Luo, X., & Ni, L. M. (2013). CSI-based indoor localization. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1300–1309.CrossRef
15.
Xi, W., Zhao, J., Li, X. -Y, Zhao, K., Tang, S., Liu, X., & Jiang, Z. (2014). Electronic frog eye: Counting crowd using WiFi. In 2014 IEEE INFOCOM (pp. 361–369).
16.
Di Domenico, S., De Sanctis, M., Cianca, E., & Bianchi, G. (2016). A trained-once crowd counting method using differential wifi channel state information. In 2016 WPA (pp. 37–42). ACM.
17.
Domenico, S. D.. Pecoraro, G., Cianca, E., & Sanctis, M. D. (2016). Trained-once device-free crowd counting and occupancy estimation using WiFi: A doppler spectrum based approach. In 2016 WiMob (pp. 1–8).
18.
Halperin, D., Hu, W., Sheth, A., & Wetherall, D. (2010). Predictable 802.11 packet delivery from wireless channel measurements. In: 2010 ACM SIGCOMM (pp. 159–170). ACM.
19.
20.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2015). SSD: Single shot multibox detector. arXiv:​1512.​02325.
Metadata
Title
Device-free crowd counting with WiFi channel state information and deep neural networks
Authors
Rui Zhou
Xiang Lu
Yang Fu
Mingjie Tang
Publication date
14-02-2020
Publisher
Springer US
Published in
Wireless Networks / Issue 5/2020
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-020-02274-7

Other articles of this Issue 5/2020

Wireless Networks 5/2020 Go to the issue

OriginalPaper

Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs

OriginalPaper

Learning-based multi-relay selection for cooperative networks based on compressed sensing

OriginalPaper

On covert throughput performance of two-way relay covert wireless communications

OriginalPaper

Exploring next generation of IOT devices compatible few mode assisting ring core elliptical cladding optical fiber

OriginalPaper

Measurement and analysis of Wi-MAX channel in 3.3 GHz using minimal-ray fading model

OriginalPaper

Throughput improvement by mode selection in hybrid duplex wireless networks