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Authors: Chris Bahnsen 1 ; David Vázquez 2 ; Antonio López 3 and Thomas Moeslund 1

Affiliations: 1 Visual Analysis of People Laboratory, Aalborg University, Denmark ; 2 Element AI, Spain ; 3 Computer Vision Center, Universitat Autònoma de Barcelona, Spain

ISBN: 978-989-758-354-4

Keyword(s): Rain Removal, Traffic Surveillance, Image Denoising.

Abstract: Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed fra mes. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance. (More)

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Paper citation in several formats:
Bahnsen, C.; Vázquez, D.; López, A. and Moeslund, T. (2019). Learning to Remove Rain in Traffic Surveillance by using Synthetic Data.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-354-4, pages 123-130. DOI: 10.5220/0007361301230130

@conference{visapp19,
author={Chris H. Bahnsen. and David Vázquez. and Antonio M. López. and Thomas B. Moeslund.},
title={Learning to Remove Rain in Traffic Surveillance by using Synthetic Data},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2019},
pages={123-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007361301230130},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Learning to Remove Rain in Traffic Surveillance by using Synthetic Data
SN - 978-989-758-354-4
AU - Bahnsen, C.
AU - Vázquez, D.
AU - López, A.
AU - Moeslund, T.
PY - 2019
SP - 123
EP - 130
DO - 10.5220/0007361301230130

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