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A Novel Approach for People Counting and Tracking from Crowd Video

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dc.contributor.authorSagun, M. Ayyuce Kizrak
dc.contributor.authorBolat, Bulent
dc.date.accessioned2026-06-27T14:14:17Z
dc.date.issued2017
dc.description.abstractCrowd analysis on video recordings is an important research area currently. In this work, a combined crowd density estimation method is presented to overcome this problem. To improve the accuracy of the system two different estimators run simultaneously and a blob is marked as a person only if both estimators mark it as person. One of the main problems in crowd density estimation is occlusion. To overcome this problem we tracked the trajectories of blobs by using a Kalman filter. The method was applied to three common benchmark data which are PETS2009, UCSD and Grand Central. The results confirm the proposed method's success.en
dc.identifier.endpage281
dc.identifier.isbn978-1-5090-5795-5
dc.identifier.startpage277
dc.identifier.urihttps://hdl.handle.net/20.500.14981/58299
dc.identifier.wos000450992400049
dc.language.isoeng
dc.publisherIEEE
dc.relation.conferenceIEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
dc.relation.ispartof2017 IEEE INTERNATIONAL CONFERENCE ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA)
dc.subjectCrowd density
dc.subjectSIFT
dc.subjectcomplex wavelet transform
dc.subjectOptical flow
dc.subjectKalman filtering
dc.subjectVideo processing
dc.subjectComputer Science
dc.titleA Novel Approach for People Counting and Tracking from Crowd Video
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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