Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82173
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dc.contributorInterdisciplinary Division of Aeronautical and Aviation Engineering-
dc.creatorCheong, KH-
dc.creatorPoeschmann, S-
dc.creatorLai, JW-
dc.creatorKoh, JM-
dc.creatorAcharya, UR-
dc.creatorYu, SCM-
dc.creatorTang, KJW-
dc.date.accessioned2020-05-05T05:58:58Z-
dc.date.available2020-05-05T05:58:58Z-
dc.identifier.urihttp://hdl.handle.net/10397/82173-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication K. H. Cheong et al., "Practical Automated Video Analytics for Crowd Monitoring and Counting," in IEEE Access, vol. 7, pp. 183252-183261, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2958255en_US
dc.subjectCrowd monitoringen_US
dc.subjectCountingen_US
dc.subjectTraffic monitoringen_US
dc.subjectData analyticsen_US
dc.subjectBackground subtractionen_US
dc.subjectSecurityen_US
dc.titlePractical automated video analytics for crowd monitoring and countingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage183252-
dc.identifier.epage183261-
dc.identifier.volume7-
dc.identifier.doi10.1109/ACCESS.2019.2958255-
dcterms.abstractVideo surveillance is gaining popularity in numerous applications, including facility management, traffic monitoring, crowd analysis, and urban security. Despite the increasing demand for closedcircuit television (CCTV) and related infrastructure in public spaces, there remains a notable lack of readily-deployable automated surveillance systems. In this study, we present a low-cost and efficient approach that integrates the use of computational object recognition to perform fully-automated identification, tracking, and counting of human traffic on camera video streams. Two software implementations are explored and the performance of these schemes is compared. Validation against controlled and non-controlled real-world environments is also demonstrated. The implementation provides automated video analytics for medium crowd density monitoring and tracking, eliminating labor-intensive tasks traditionally requiring human operation, with results indicating great reliability in real-life scenarios.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 6 Dec. 2019, v. 7, p. 183252-183261-
dcterms.isPartOfIEEE access-
dcterms.issued2019-
dc.identifier.isiWOS:000509527200038-
dc.identifier.scopus2-s2.0-85078045243-
dc.identifier.eissn2169-3536-
dc.description.validate202006 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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