Visibility cameras: where and how to look

Authors:
Nathan Graves;Shawn Newsam
Affiliations:
University of California at Merced, Merced, CA, USA;University of California at Merced, Merced, CA, USA
Venue:
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Year:
2012

Citing 6
Cited 1

Vision and the Atmosphere

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Blind Haze Separation

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Single image dehazing

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Using visibility cameras to estimate atmospheric light extinction

WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Shedding light on the weather

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Contrast restoration of weather degraded images

IEEE Transactions on Pattern Analysis and Machine Intelligence

Multimedia analysis for ecological data

Proceedings of the 20th ACM international conference on Multimedia

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Abstract

This paper investigates image processing and pattern recognition techniques to estimate light extinction based on the visual content of images from static cameras. We propose two predictive models that incorporate multiple scene regions into the estimation: regression trees and multivariate linear regression. Incorporating multiple regions is important since regions at different distances are effective for estimating light extinction under different visibility regimes. We evaluate our models using a sizable dataset of images and ground truth light extinction values from a visibility camera system in Phoenix, Arizona.