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Indoor Crowd Density Estimation through Mobile Smartphone Wi-Fi Probes

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成果类型:
期刊论文
作者:
Tang, Xiaoyong*;Xiao, Bin;Li, Kenli
通讯作者:
Tang, Xiaoyong
作者机构:
[Tang, Xiaoyong] Hunan Agr Univ, Informat Sci & Technol Coll, Southern Reg Collaborat Innovat Ctr Grain & Oil C, Changsha 410128, Peoples R China.
[Tang, Xiaoyong; Li, Kenli] Hunan Univ, Natl Supercomp Ctr Changsha, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China.
[Xiao, Bin] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China.
通讯机构:
[Tang, Xiaoyong] H
Hunan Agr Univ, Informat Sci & Technol Coll, Southern Reg Collaborat Innovat Ctr Grain & Oil C, Changsha 410128, Peoples R China.
语种:
英文
关键词:
Wireless fidelity;Estimation;Probes;Monitoring;Heuristic algorithms;Feature extraction;Wireless communication;Crowd density;indoor positioning algorithm;received signal strength indicator (RSSI);smartphones;Wi-Fi probe
期刊:
IEEE Transactions on Systems, Man, and Cybernetics: Systems
ISSN:
2168-2216
年:
2018
卷:
50
期:
7
页码:
2638-2649
基金类别:
Manuscript received December 11, 2017; accepted April 2, 2018. Date of publication April 25, 2018; date of current version June 16, 2020. This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0201402, in part by the National Science Foundation of China under Grant 61370098, Grant 61672219, and Grant 61772446, and in part by the Hunan Provincial Natural Science Foundation of China under Grant 2015JJ2078. This paper was recommended by Associate Editor D. Akopian. (Corresponding author: Xiaoyong Tang.) X. Tang is with the Information Science and Technology College, Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China, Hunan Agricultural University, Changsha 410128, China, and also with the College of Computer Science and Electronic Engineering, National Supercomputing Center in Changsha, Hunan University, Changsha 410082, China (e-mail: tang_313@163.com).
机构署名:
本校为第一且通讯机构
院系归属:
信息科学技术学院
摘要:
Crowd density estimation is one of the critical issues in social activities. The traditional solution to this problem is to leverage video surveillance to monitor a crowd. However, this is not accurate for crowd density estimation because it is still hard to identify people from background. In the past few years, more and more people use Wi-Fi enabled smartphones. Smartphones can send Wi-Fi request packets periodically, even when they are not connected to access points. This gives another promising solution to the crowd density estimation even for the public environment. In this paper, we firs...

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