Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84352
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Type: Conference paper
Title: Probabilistic parameter selection for learning scene structure from video
Author: Breitenstein, M.
Sommerlade, E.
Leibe, B.
Van Gool, L.
Reid, I.
Citation: Proceedings of the British Machine Vision Conference 1-4 September 2008 / M. Everingham and C. Needham (eds.): pp.32.1-32.10
Publisher: BMVA Press
Publisher Place: UK
Issue Date: 2008
ISBN: 9781901725360
Conference Name: British Machine Vision Conference (19th : 2008 : Leeds, UK)
Statement of
Responsibility: 
M. D. Breitenstein, E. Sommerlade, B. Leibe, L. Van Gool, I. Reid
Abstract: We 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.
Rights: © BMVA August 2008
RMID: 0020131256
DOI: 10.5244/C.22.32
Description (link): http://www.comp.leeds.ac.uk/bmvc2008/proceedings/index.html
Appears in Collections:Computer Science publications

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