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
this https URL

通过使用监视摄像头，设计了一个系统来检测降雨。使用 3D 卷积的现代 CNN 方法与先前的最新方法进行比较，结果表明，选择感兴趣区域对性能有很大的影响，并且该提议的 3D CNN 在所有指标上都表现出比以前的最新方法更好的表现。