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AuthorAbdou, Mohamed
AuthorErradi, Abdelkarim
Available date2023-04-10T09:10:03Z
Publication Date2020
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089594
URIhttp://hdl.handle.net/10576/41797
AbstractCrowd counting is applied in many areas including efficient resources allocation and effective management of emergency situations. In this paper, we survey and compare various crowd counting methods. Additionally, we identify the limitations of existing approaches and sketch an agenda for future work to address the identified open research challenges. Furthermore, we present an enhanced deep learning-based solution for crowd counting at bus stops. 2020 IEEE.
SponsorACKNOWLEDGMENT This research was made possible by UREP24-125-1-030 grant from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectcrowd behavior analysis
crowd counting
crowd counting at bus stops
machine learning
TitleCrowd Counting: A Survey of Machine Learning Approaches
TypeConference Paper
Pagination48-54
dc.accessType Abstract Only


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