On the pedestrian flow analysis through passive wifi sensing

Huang, B; Li, X; Mao, G; Jia, B; Li, W

On the pedestrian flow analysis through passive wifi sensing

Huang, B Li, X Mao, G

Jia, B Li, W

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings, 2020, 00, pp. 1-6
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Huang, B
dc.contributor.author	Li, X
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949
dc.contributor.author	Jia, B
dc.contributor.author	Li, W
dc.date	2019-12-09
dc.date.accessioned	2021-04-12T09:09:01Z
dc.date.available	2021-04-12T09:09:01Z
dc.date.issued	2020
dc.identifier.citation	2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings, 2020, 00, pp. 1-6
dc.identifier.isbn	9781728109626
dc.identifier.uri	http://hdl.handle.net/10453/148035
dc.description.abstract	© 2019 IEEE. The proliferation of mobile devices, including smartphones and tablets, has been enabling new possibilities for inferring information about the positions, behavior and activities of the users carrying these devices. For instance, by leveraging the WiFi probes sent out by mobile devices in public spaces (such as shopping malls, metro stations, etc.), even if pedestrians do not have their mobile devices to be associated with any WiFi access point (AP), it is attractive to conduct pedestrian analysis in a passive sensing approach to facilitate the efficient management of public infrastructures as well as convenient customer services. This paper considers the problem of pedestrian flow analysis by implementing a pedestrian surveillance system in the transfer channel of a metro station in Guangzhou China. Firstly, a fingerprint database is generated through a Gaussian process regression (GPR) approach. On these grounds, a pedestrian number estimation method based on linear regression is presented by making use of the fingerprint-based localization method to refine the number of mobile devices residing in the surveillance area, and a pedestrian velocity estimation method is proposed based on particle filter and the inverse distance weighted (IDW) method. According to the dataset obtained in real scenarios, the effectiveness and advantages of the proposed two methods are confirmed.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
dc.relation.ispartof	IEEE Global Communications Conference
dc.relation.isbasedon	10.1109/GLOBECOM38437.2019.9013812
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	On the pedestrian flow analysis through passive wifi sensing
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Waikoloa, HI, USA
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-12T09:09:00Z
pubs.finish-date	2019-12-13
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-12-09
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. The proliferation of mobile devices, including smartphones and tablets, has been enabling new possibilities for inferring information about the positions, behavior and activities of the users carrying these devices. For instance, by leveraging the WiFi probes sent out by mobile devices in public spaces (such as shopping malls, metro stations, etc.), even if pedestrians do not have their mobile devices to be associated with any WiFi access point (AP), it is attractive to conduct pedestrian analysis in a passive sensing approach to facilitate the efficient management of public infrastructures as well as convenient customer services. This paper considers the problem of pedestrian flow analysis by implementing a pedestrian surveillance system in the transfer channel of a metro station in Guangzhou China. Firstly, a fingerprint database is generated through a Gaussian process regression (GPR) approach. On these grounds, a pedestrian number estimation method based on linear regression is presented by making use of the fingerprint-based localization method to refine the number of mobile devices residing in the surveillance area, and a pedestrian velocity estimation method is proposed based on particle filter and the inverse distance weighted (IDW) method. According to the dataset obtained in real scenarios, the effectiveness and advantages of the proposed two methods are confirmed.

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http://hdl.handle.net/10453/148035