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Beyond static detectors

Beyond static detectors: a Bayesian approach to fusing long-term motion with appearance for robust people detection in highly cluttered scenes

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Original languageEnglish
Title of host publicationProceedings of the Sixth International IEEE Workshop on Visual Surveillance
EditorsPascal Fua, Stephen Maybank, Graeme A. Jones
Place of PublicationKingston-Upon-Thames
PublisherIEEE
Pages121-128
Number of pages8
ISBN (Print)9780955300301, 0955300304
StatePublished - 2006
Event6th IEEE International Workshop on Visual Surveillance - Graz, Austria

Conference

Conference6th IEEE International Workshop on Visual Surveillance
CountryAustria
CityGraz
Period13/05/0613/05/06

Abstract

In this work we present a framework for robust people de-tection in highly cluttered scenes with low resolution im-age sequences. Our model utilises both human appearance and their long-term motion information through a fusion formulated in a Bayesian framework. In particular, peo-ple appearance is modeled by histograms of oriented gra-dients. Motion information is computed via an improved background modeling by spatial motion constrains. Exper-iments demonstrate that our method reduces significantly the false positive rate compared to that of a state of the art human detector under very challenging conditions.

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