Accurate and high spatiotemporal resolution rainfall observations are essential for hydrological forecasting and flood management, especially in urban hydrological applications. However, it is difficult for traditional rainfall gauges, weather radars, and satellites to accurately estimate rainfall while simultaneously capturing the spatial and temporal variability of rainfall well. In this context, video-based rainfall measurement, a novel method, has the advantages of real-time performance and low cost and may thus provide a new way to establish rainfall observation networks with high spatial and temporal resolution. In recent years, different algorithms have been developed to recognize raindrops and estimate rainfall from rainfall videos. It has been demonstrated that video-based rainfall measurement methods can provide comprehensive rainfall information with fine spatial and temporal granularity. However, raindrop visibility and the depth of field effects are difficult to address. The motion blur effect of raindrops may result in substantial errors and uncertainties. A fundamental problem of video-based rainfall measurements lies in locating raindrops and accurately calculating their actual size. Moreover, the effectiveness of deep learning-based video rainfall measurement models is greatly influenced by the diversity of the training data. Therefore, enhancing the high robustness and accuracy of video-based rainfall measurement algorithms and increasing the computational efficiency are paramount to further development, which are prerequisites for their application in practical rainfall monitoring and developing multicamera monitoring networks.

中文翻译：

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基于视频的降雨测量方法综述

准确、高时空分辨率的降雨观测对于水文预报和洪水管理至关重要，特别是在城市水文应用中。然而，传统的雨量计、天气雷达和卫星很难准确估算降雨量，同时很好地捕捉降雨的时空变化。在此背景下，基于视频的降雨测量作为一种新颖的方法，具有实时性和低成本的优势，可能为建立高时空分辨率的降雨观测网络提供新的途径。近年来，已经开发了不同的算法来识别雨滴并从降雨视频中估计降雨量。事实证明，基于视频的降雨测量方法可以提供具有精细时空粒度的综合降雨信息。然而，雨滴可见度和景深效应很难解决。雨滴的运动模糊效应可能会导致重大错误和不确定性。基于视频的降雨测量的一个基本问题在于定位雨滴并准确计算其实际大小。此外，基于深度学习的视频雨量测量模型的有效性很大程度上受到训练数据多样性的影响。因此，提高基于视频的降雨测量算法的鲁棒性和准确性，提高计算效率对于进一步发展至关重要，这是其在实际降雨监测中应用和开发多摄像机监测网络的先决条件。