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dc.contributor.authorBreitenstein, M.en
dc.contributor.authorSommerlade, E.en
dc.contributor.authorLeibe, B.en
dc.contributor.authorVan Gool, L.en
dc.contributor.authorReid, I.en
dc.identifier.citationProceedings of the British Machine Vision Conference 1-4 September 2008 / M. Everingham and C. Needham (eds.): pp.32.1-32.10en
dc.description.abstractWe present an online learning approach for robustly combining unreliable observations from a pedestrian detector to estimate the rough 3D scene geometry from video sequences of a static camera. Our approach is based on an entropy modelling framework, which allows to simultaneously adapt the detector parameters, such that the expected information gain about the scene structure is maximised. As a result, our approach automatically restricts the detector scale range for each image region as the estimation results become more confident, thus improving detector run-time and limiting false positives.en
dc.description.statementofresponsibilityM. D. Breitenstein, E. Sommerlade, B. Leibe, L. Van Gool, I. Reiden
dc.publisherBMVA Pressen
dc.rights© BMVA August 2008en
dc.titleProbabilistic parameter selection for learning scene structure from videoen
dc.typeConference paperen
dc.contributor.conferenceBritish Machine Vision Conference (19th : 2008 : Leeds, UK)en
dc.identifier.orcidReid, I. [0000-0001-7790-6423]en
Appears in Collections:Computer Science publications

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