Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84204
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dc.contributor.authorBenfold, B.en
dc.contributor.authorReid, I.en
dc.date.issued2011en
dc.identifier.citationProceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 11) / pp. 3457-3464en
dc.identifier.isbn9781457703942en
dc.identifier.issn1063-6919en
dc.identifier.urihttp://hdl.handle.net/2440/84204-
dc.description.abstractThe majority of existing pedestrian trackers concentrate on maintaining the identities of targets, however systems for remote biometric analysis or activity recognition in surveillance video often require stable bounding-boxes around pedestrians rather than approximate locations. We present a multi-target tracking system that is designed specifically for the provision of stable and accurate head location estimates. By performing data association over a sliding window of frames, we are able to correct many data association errors and fill in gaps where observations are missed. The approach is multi-threaded and combines asynchronous HOG detections with simultaneous KLT tracking and Markov-Chain Monte-Carlo Data Association (MCM-CDA) to provide guaranteed real-time tracking in high definition video. Where previous approaches have used ad-hoc models for data association, we use a more principled approach based on a Minimal Description Length (MDL) objective which accurately models the affinity between observations. We demonstrate by qualitative and quantitative evaluation that the system is capable of providing precise location estimates for large crowds of pedestrians in real-time. To facilitate future performance comparisons, we make a new dataset with hand annotated ground truth head locations publicly available.en
dc.description.statementofresponsibilityBen Benfold and Ian Reiden
dc.description.urihttp://cvpr2011.org/index.htmlen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsCopyright status unknownen
dc.titleStable multi-target tracking in real-time surveillance videoen
dc.typeConference paperen
dc.identifier.rmid0020131167en
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (24th : 2011 : Colorado Springs, CO, U.S.A.)en
dc.identifier.doi10.1109/CVPR.2011.5995667en
dc.publisher.placeUSAen
dc.identifier.pubid18431-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidReid, I. [0000-0001-7790-6423]en
Appears in Collections:Computer Science publications

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