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Design and Implementation of Non-intrusive Stationary Occupancy Count in Elevator with WiFi

  • Wei ShiEmail author
  • Umer Tahir
  • Hui Zhang
  • Jizhong Zhao
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 303)

Abstract

Wi-Fi Sensing has shown huge progress in last few years. Multiple Input and Multiple Output (MIMO) has opened a gateway of new generation of sensing capabilities. This can also be used as a passive surveillance technology which is non-intrusive meaning it is not a nuisance as it is not need the subjects to carry any dedicated device. In this thesis, we present a way to count crowd in the elevator non-intrusively with 5 GHz Wi-Fi signals. For this purpose, Channel State Information (CSI) is collected from the commercially available off-the-shelf (COTS) Wi-Fi devices setup in an elevator. Our goal is to Analyze the CSI of every subcarrier frequency and then count the occupancy in it with the help of Convolutional Neural Network (CNN). After CSI data collection, we normalize the data with Savitzky Golay method. Each CSI subcarrier data of all the samples is made mean centered and then outliers are removed by applying Hampel Filter. The resultant wave is decimated and divided into 5 equal length segments representing the human presence recorded in 5 s. Continuous wavelet frequency representations are generated for all segments of every CSI sub-carrier frequency waves. These frequency pattern images are then fed to the CNN model to generalize and classify what category of crowd they belong to. After training, the model can achieve the test accuracy of more than 90%.

Keywords

Wi-Fi Sensing CSI CWT CNN 

References

  1. 1.
    Li, M., Zhang, Z., Huang, K., Tan, T.: Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection. In: ICPR 2008. IEEE (2008)Google Scholar
  2. 2.
    Nichols, J.D., et al.: Multi-scale occupancy estimation and modelling using multiple detection methods. J. Appl. Ecol. 45(5), 1321–1329 (2008)CrossRefGoogle Scholar
  3. 3.
    Lin, S.-F., Chen, J.-Y., Chao, H.-X.: Estimation of number of people in crowded scenes using perspective transformation. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 31(6), 645–654 (2001)CrossRefGoogle Scholar
  4. 4.
    Kim, M., Kim, W., Kim, C.: Estimating the number of people in crowded scenes. In: Proceedings of the IS&T/SPIE Electronic Imaging, p. 78820L (2011)Google Scholar
  5. 5.
    Weppner, J., Lukowicz, P.: Bluetooth based collaborative crowd density estimation with mobile phones. In: PerCom 2013. IEEE (2013)Google Scholar
  6. 6.
    Schauer, L., Werner, M., Marcus, P.: Estimating crowd densities and pedestrian flows using Wi-Fi and Bluetooth. In: Mobiquitous 2014. ICST (2014)Google Scholar
  7. 7.
    Wang, J., Katabi, D.: Dude, where’s my card?: RFID positioning that works with multipath and NOLS. In: Proceedings of ACM SIGCOMM (2013)Google Scholar
  8. 8.
    Wang, J., Vasisht, D., Katabi, D.: RF-IDraw: virtual touch screen in the air using RF signals. In: Proceedings of ACM SIGCOMM (2015)Google Scholar
  9. 9.
    Wang, J., Xiong, J., Jiang, H., Chen, X., Fang, D.: D-Watch: embracing “Bad” multipaths for device-free localization with COTS RFID devices, pp. 253–266 (2016)Google Scholar
  10. 10.
    Wei, T., Zhang, X.: Gyro in the air: tracking 3D orientation of batteryless internet-of-things. In: Proceedings of ACM MobiCom, pp. 55–68 (2016)Google Scholar
  11. 11.
    Yang, L., Chen, Y., Li, X.-Y., Xiao, C., Li, M., Liu, Y.: Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices. In: Proceedings of ACM MobiCom (2014)Google Scholar
  12. 12.
    Ding, H., et al.: Human object estimation via backscattered radio frequency signal. In: INFOCOM 2015. IEEE (2015)Google Scholar
  13. 13.
    Wirz, M., Franke, T., Roggen, D., Mitleton-Kelly, E., Lukowicz, P., Troster, G.: Probing crowd density through smartphones in city-scale mass gatherings. EPJ Data Sci. 2(1), 1 (2013)CrossRefGoogle Scholar
  14. 14.
    Lam, K.P., et al.: Occupancy detection through an extensive environmental sensor network in an open-plan office building. IBPSA Build. Simul. 145, 1452–1459 (2009)Google Scholar
  15. 15.
    Jiang, C., Masood, M.K., Soh, Y.C., Li, H.: Indoor occupancy estimation from carbon dioxide concentration. Energy Build. 131, 132–141 (2016)CrossRefGoogle Scholar
  16. 16.
    Wang, S., Burnett, J., Chong, H.: Experimental validation of CO2-based occupancy detection for demand-controlled ventilation. Indoor Built Environ. 8(6), 377–391 (2000)CrossRefGoogle Scholar
  17. 17.
    Depatla, S., Muralidharan, A., Mostofi, Y.: Occupancy estimation using only WiFi power measurements. IEEE J. Sel. Areas Commun. 33(7), 1381–1393 (2015)CrossRefGoogle Scholar
  18. 18.
    Choi, J.W., Quan, X., Cho, S.H.: Bi-directional passing people counting system based on IR-UWB radar sensors. IEEE Internet Things J. 5, 512–522 (2017)CrossRefGoogle Scholar
  19. 19.
    Mohammadmoradi, H., Yin, S., Gnawali, O.: Room occupancy estimation through WiFi, UWB, and light sensors mounted on doorways. In: Proceedings of the 2017 International Conference on Smart Digital Environment, pp. 27–34 (2017)Google Scholar
  20. 20.
    Lv, H., et al.: Multi-target human sensing via UWB bio-radar based on multiple antennas. In: Proceedings of the IEEE TENCON, pp. 1–4 (2013)Google Scholar
  21. 21.
    He, J., Arora, A.: A regression-based radar-mote system for people counting. In: Proceedings of the IEEE PerCom, pp. 95–102 (2014)Google Scholar
  22. 22.
    Abdelnasser, H., Youssef, M., Harras, K.A.: WiGest: a ubiquitous WiFi-based gesture recognition system. In: Proceedings of IEEE INFOCOM (2015)Google Scholar
  23. 23.
    Chen, B., Yenamandra, V., Srinivasan, K.: Tracking keystrokes using wireless signals. In: Proceedings of ACM MobiSys (2015)Google Scholar
  24. 24.
    Ding, H., et al.: RFIPad: enabling cost-efficient and device-free in-air handwriting using passive tags. In: Proceedings of IEEE ICDCS (2017)Google Scholar
  25. 25.
    Li, H., Yang, W., Wang, J., Xu, Y., Huang, L.: WiFinger: talk to your smart devices with finger-grained gesture. In: Proceedings of ACM UbiComp (2016)Google Scholar
  26. 26.
    Lien, J., et al.: Soli: ubiquitous gesture sensing with millimeter wave radar. ACM Trans. Graph. 35(4), 142 (2016)CrossRefGoogle Scholar
  27. 27.
    Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: Proceedings of ACM MobiCom (2013)Google Scholar
  28. 28.
    Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: Understanding and modeling of WiFi signal based human activity recognition. In: Proceedings of ACM MobiCom (2015)Google Scholar
  29. 29.
    Xi, W., et al.: Electronic frog eye: counting crowd using WiFi. In: INFOCOM 2014. IEEE (2014)Google Scholar
  30. 30.
    Xu, C., et al.: SCPL: indoor device-free multi-subject counting and localization using radio signal strength. In: IPSN 2013. IEEE (2013)Google Scholar
  31. 31.
    Zheng, Y., Zhou, Z., Liu, Y.: From RSSI to CSI: indoor localization via channel response. ACM Comput. Surv. 46(2), 1–32 (2013)zbMATHGoogle Scholar
  32. 32.
    Adib, F., Katabi, D.: See through walls with WiFi! ACM SIGCOMM Comput. Commun. Rev. 43(4), 75–86 (2013)CrossRefGoogle Scholar
  33. 33.
    Nakatani, T., Maekawa, T., Shirakawa, M., et al.: Estimating the physical distance between two locations with Wi-Fi received signal strength information using obstacle-aware approach. Proc. ACM on Interact. Mob. Wearable Ubiquit. Technol. 2(3), 1–26 (2018)CrossRefGoogle Scholar
  34. 34.
    Adib, F.: MIT. Wi-Vi: See Through Walls with Wi-Fi Signals [EB/OL], August 2013. http://people.csail.mit.edu/fadel/wivi/radar.png
  35. 35.
    GitLab: PicoScenes Installation [EB/OL], 11 August 2018. http://gitlab.com/wifisensing/PicoScenes-Setup/
  36. 36.
    Tamas, L., Lazea, G.: Pattern recognition and tracking dynamic objects with LIDAR. In: Robotics. VDE (2011)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Xi’an Jiaotong UniversityXi’anPeople’s Republic of China

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