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dc.contributor.authorAribilola, Ifeoluwapo
dc.contributor.authorAsghar, Mamoona
dc.contributor.authorKanwal, Nadia
dc.contributor.authorAnsari, Mohammad Samar
dc.contributor.authorLee, Brian
dc.date.accessioned2022-09-23T13:54:05Z
dc.date.available2022-09-23T13:54:05Z
dc.date.issued2022-07-19
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/627192/xFItEO-AFOM_Advanced_Flow_of_Motion_Detection_Algorithm_for_Dynamic_Camera_Videos.pdf?sequence=1
dc.identifier.citationAribilola, I., Asghar, M. N., Kanwal, N., Ansari, M. S., & Lee, B. (2022, 9-10 June 2022). AFOM: Advanced Flow of Motion detection algorithm for dynamic camera videos. 33rd Irish Signals and Systems Conference (ISSC). Cork, Ireland.en_US
dc.identifier.doi10.1109/ISSC55427.2022.9826141
dc.identifier.urihttp://hdl.handle.net/10034/627192
dc.description©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.description.abstractThe surveillance videos taken from dynamic cam-eras are susceptible to multiple security threats like replay attacks, man-in-the-middle attacks, pixel correlation attacks etc. Using unsupervised learning, it is a challenge to detect objects in such surveillance videos, as fixed objects may appear to be in motion alongside the actual moving objects. But despite this challenge, the unsupervised learning techniques are efficient as they save object labelling and model training time, which is usually a case with supervised learning models. This paper proposes an effective computer vision-based object identification algorithm that can detect and separate stationary objects from moving objects in such videos. The proposed Advanced Flow Of Motion (AFOM) algorithm takes advantage of motion estimation between two consecutive frames and induces the estimated motion back to the frame to provide an improved detection on the dynamic camera videos. The comparative analysis demonstrates that the proposed AFOM outperforms a traditional dense optical flow (DOF) algorithm with an average increased difference of 56 % in accuracy, 61 % in precision, and 73 % in pixel space ratio (PSR), and with minimal higher object detection timing.en_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/9826141en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectHeuristic algorithmsen_US
dc.subjectSurveillanceen_US
dc.subjectComputational modelingen_US
dc.subjectDynamicsen_US
dc.subjectCamerasen_US
dc.subjectMotion detectionen_US
dc.subjectObject recognitionen_US
dc.titleAFOM: Advanced Flow of Motion Detection Algorithm for Dynamic Camera Videosen_US
dc.typeConference Proceedingen_US
dc.contributor.departmentTechnological University of the Shannon; National University of Ireland; University of Keele; University of Chesteren_US
or.grant.openaccessYesen_US
rioxxterms.funderUnfundeden_US
rioxxterms.identifier.projectUnfundeden_US
rioxxterms.versionAMen_US
rioxxterms.versionofrecord10.1109/ISSC55427.2022.9826141en_US
dcterms.dateAccepted2022-05-01
rioxxterms.publicationdate2022-07-19
dc.date.deposited2022-09-23en_US


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