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Statistics Algorithm and Application of People Flow Detection Based on Wi-Fi Probe

  • Wenbin Zheng
  • Xiao Liu
  • Peng LiEmail author
  • Li Lin
  • Hancong Wang
Conference paper
  • 25 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)

Abstract

For the open laboratory management of an university, the attendance data is not accurate, and meanwhile, the statistics data is too easy to be lacked by using the traditional devices. Wi-Fi probe for people flow detection is often used for safety warnings in public places and commercial promotion of products, laboratory attendance and so on. However, the existing technologies used for laboratory attendance only identify the presence of a target while neglect the fact that when the coverage range of Wi-Fi signal exceeds the attendance range, the attendance data obtained by Wi-Fi probe will be inaccurate. So far, Wi-Fi probe has not been applied for managing people flow of open laboratory. In this paper, we propose an algorithm based on Wi-Fi probe so as to successfully increase the quality of people flow detection in open laboratory. We use RSSI positioning theory to distinguish people inside and outside the laboratory and identify the statistics of the people flow. The experimental results also demonstrate our system not only has higher accuracy than the existing RFID attendance systems, but also provide the rank of time which people spent in the laboratory, the numbers of people and the total time which all the people spend in the lab in a certain time. Managers and users can log in the system to check the statistical data. The system improves the utilization of the laboratory and contributes to the development of open laboratory management practices.

Keywords

Wi-Fi probe People flow detection Laboratory management 

Notes

Acknowledgement

In this paper, the research was supported by Scientific Research Fund of Fujian Provincial Education Department (JAT160339, JT180340) and by Research Project of Experimental Teaching Reform in Fujian University of Technology (SJ2017003).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Wenbin Zheng
    • 1
    • 2
  • Xiao Liu
    • 1
  • Peng Li
    • 1
    • 2
    Email author
  • Li Lin
    • 1
    • 2
  • Hancong Wang
    • 1
    • 2
  1. 1.School of Information Science and EngineeringFujian University of TechnologyFuzhouChina
  2. 2.National Demonstration Center for Experimental Electronic Information and Electrical Technology EducationFujian University of TechnologyFuzhouChina

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