77 Downloads (Pure)

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 languageEnglish
Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition Workshops
PublisherIEEE
Publication dateJun 2019
Pages55-64
Publication statusPublished - Jun 2019
Event2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) - Long Beach, United States
Duration: 16 Jun 201920 Jun 2019

Conference

Conference2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
CountryUnited States
CityLong Beach
Period16/06/201920/06/2019
SeriesIEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISSN2160-7516

Fingerprint Dive into the research topics of 'Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras'. Together they form a unique fingerprint.

Cite this