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In integrated surveillance systems based on visual cameras, the mitigation ofadverse weather conditions is an active research topic. Within this field, rainremoval algorithms have been developed that artificially remove rain streaksfrom images or video. In order to deploy such rain removal algorithms in asurveillance setting, one must detect if rain is present in the scene. In thispaper, we design a system for the detection of rainfall by the use ofsurveillance cameras. We reimplement the former state-of-the-art method forrain detection and compare it against a modern CNN-based method by utilizing 3Dconvolutions. The two methods are evaluated on our new AAU Visual Rain Dataset(VIRADA) that consists of 215 hours of general-purpose surveillance video fromtwo traffic crossings. The results show that the proposed 3D CNN outperformsthe previous state-of-the-art method by a large margin on all metrics, for bothof the traffic crossings. Finally, it is shown that the choice ofregion-of-interest has a large influence on performance when trying togeneralize the investigated methods. The AAU VIRADA dataset and ourimplementation of the two rain detection algorithms are publicly available athttps://bitbucket.org/aauvap/aau-virada.

Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras