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dc.contributor.authorErichson, Nils Benjamin
dc.contributor.authorDonovan, Carl Robert
dc.date.accessioned2017-02-13T00:32:40Z
dc.date.available2017-02-13T00:32:40Z
dc.date.issued2016-05
dc.identifier.citationErichson , N B & Donovan , C R 2016 , ' Randomized low-rank Dynamic Mode Decomposition for motion detection ' Computer Vision and Image Understanding , vol. 146 , pp. 40-50 . https://doi.org/10.1016/j.cviu.2016.02.005en
dc.identifier.issn1077-3142
dc.identifier.otherPURE: 241020394
dc.identifier.otherPURE UUID: 083c43ad-3499-41c3-ae32-b965eb75a6fd
dc.identifier.otherRIS: urn:E5856BD20AE194804E9E12DBDBB99D6B
dc.identifier.otherScopus: 84959450462
dc.identifier.urihttp://hdl.handle.net/10023/10273
dc.descriptionN. Benjamin Erichson acknowledges support from the UK Engineering and Physical Sciences Research Council (EPSRC).en
dc.description.abstractThis paper introduces a fast algorithm for randomized computation of a low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this matrix to represent the development of a spatial grid through time e.g. data from a static video source. DMD was originally introduced in the fluid mechanics community, but is also suitable for motion detection in video streams and its use for background subtraction has received little previous investigation. In this study we present a comprehensive evaluation of background subtraction, using the randomized DMD and compare the results with leading robust principal component analysis algorithms. The results are convincing and show the random DMD is an efficient and powerful approach for background modeling, allowing processing of high resolution videos in real-time. Supplementary materials include implementations of the algorithms in Python.
dc.language.isoeng
dc.relation.ispartofComputer Vision and Image Understandingen
dc.rights© 2016, Publisher / the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.sciencedirect.com / https://dx.doi.org/10.1016/j.cviu.2016.02.005en
dc.subjectDynamic Mode Decompositionen
dc.subjectRobust principal component analysisen
dc.subjectRandomized singular value decompositionen
dc.subjectMotion detectionen
dc.subjectBackground subtractionen
dc.subjectVideo surveillanceen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectDASen
dc.subject.lccQA75en
dc.titleRandomized low-rank Dynamic Mode Decomposition for motion detectionen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.identifier.doihttps://doi.org/10.1016/j.cviu.2016.02.005
dc.description.statusPeer revieweden
dc.date.embargoedUntil2017-02-12


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