Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/84320
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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 |
DOI: | 10.1109/CVPR.2008.4587522 |
Published version: | http://dx.doi.org/10.1109/cvpr.2008.4587522 |
Appears in Collections: | Aurora harvest Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.