Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84320
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Type: Conference paper
Title: Information-theoretic active scene exploration
Author: Sommerlade, E.
Reid, I.
Citation: IEEE Conference on Computer Vision and Pattern Recognition, 2008 (CVPR 2008) / pp.1-7
Publisher: IEEE
Publisher Place: USA
Issue Date: 2008
ISBN: 9781424422425
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (21st : 2008 : Anchorage, AK)
Statement of
Responsibility: 
Eric Sommerlade, Ian Reid
Abstract: Studies support the need for high resolution imagery to identify persons in surveillance videos. However, the use of telephoto lenses sacrifices a wider field of view and thereby increases the uncertainty of other, possibly more interesting events in the scene. Using zoom lenses offers the possibility of enjoying the benefits of both wide field of view and high resolution, but not simultaneously. We approach this problem of balancing these finite imaging resources - or of exploration vs exploitation - using an information-theoretic approach. We argue that the camera parameters - pan, tilt and zoom - should be set to maximise information gain, or equivalently minimising conditional entropy of the scene model, comprised of multiple targets and a yet unobserved one. The information content of the former is supplied directly by the uncertainties computed using a Kalman filter tracker, while the latter is modelled using a rdquobackgroundrdquo Poisson process whose parameters are learned from extended scene observations; together these yield an entropy for the scene. We support our argument with quantitative and qualitative analyses in simulated and real-world environments, demonstrating that this approach yields sensible exploration behaviours in which the camera alternates between obtaining close-up views of the targets while paying attention to the background, especially to areas of known high activity.
Rights: ©2008 IEEE
RMID: 0020131267
DOI: 10.1109/CVPR.2008.4587522
Appears in Collections:Computer Science publications

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