Abstract
In integrated surveillance systems based on visual cameras, the mitigation of adverse weather conditions is an active research topic. Within this field, rain removal algorithms have been developed that artificially remove rain streaks from images or video. In order to deploy such rain removal algorithms in a surveillance setting, one must detect if rain is present in the scene. In this paper, we design a system for the detection of rainfall by the use of surveillance cameras. We reimplement the former state-of-the-art method for rain detection and compare it against a modern CNN-based method by utilizing 3D convolutions. The two methods are evaluated on our new AAU Visual Rain Dataset (VIRADA) that consists of 215 hours of general-purpose surveillance video from two traffic crossings. The results show that the proposed 3D CNN outperforms the previous state-of-the-art method by a large margin on all metrics, for both of the traffic crossings. Finally, it is shown that the choice of region-of-interest has a large influence on performance when trying to generalize the investigated methods. The AAU VIRADA dataset and our implementation of the two rain detection algorithms are publicly available at https://bitbucket.org/aauvap/aau-virada
Original language | English |
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Title of host publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops |
Publisher | IEEE |
Publication date | Jun 2019 |
Pages | 55-64 |
Publication status | Published - Jun 2019 |
Event | 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) - Long Beach, United States Duration: 16 Jun 2019 → 20 Jun 2019 |
Conference
Conference | 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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Country/Territory | United States |
City | Long Beach |
Period | 16/06/2019 → 20/06/2019 |
Series | IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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ISSN | 2160-7516 |
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AAU Visual Rain Dataset (VIRADA)
Haurum, J. B. (Creator), Bahnsen, C. H. (Creator) & Moeslund, T. B. (Creator), Zenodo, 16 Jun 2019
DOI: 10.5281/zenodo.4715681, https://zenodo.org/record/4715681
Dataset