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.

翻译：在以视像摄像机为基础的综合监测系统中,减轻恶劣天气条件是一个积极的研究课题。在这一领域,已经开发出人工去除图象或视频雨迹的雨水清除算法。为了在监视环境中部署这样的雨水清除算法,我们必须检测现场是否有雨水。在本文件中,我们设计了一个通过使用监视摄像机探测降雨的系统。我们通过利用3D Convolutions,执行以前的最先进的雨水探测方法,并将其与现代CNN方法进行比较。我们的新AAAU视觉雨数据集(VIRADA)对这两种方法进行了评估,其中包括两个交通过境点的215小时一般用途监视录像。结果显示,拟议的3DCNN在两个交通过境点上大幅度地超越了以前所有标准上的最新方法。最后,我们表明,在试图普及调查方法时,选择区域对业绩有很大影响。AAAAU VIRADA数据集和我们执行两种雨探测/VARA的算法是公开的。https://wwwvirabia。