基于社会监控视频的雨量反演

针对利用机器视觉算法估算雨量低准确率的问题，提出基于社会监控视频的雨量反演算法。首先利用降雨分类网络剔除无雨视频；其次引入交替方向乘子法(alternating direction method of multipliers, ADMM)提取降雨视频的前景信息，并通过语义分割和背景差分方法选取感兴趣区域(region of interest, ROI);然后构建以灰度变化和饱和度为特征的高斯混合模型(Gaussian mixture model, GMM)筛选ROI区域内的雨滴；最后依据透视成像关系计算雨滴尺寸，使用气象学Gamma模型反演降雨量。实验结果表明，本文降雨分类方法的准确率在MWD (multi-class weather dataset)到达91.3%,在真实的数据集到达77.0%,雨量估算结果相比于现有方法更为准确。

Rainfall inversion based on social surveillance video

Aiming at the issue of low accuracy of rainfall estimation by the machine vision algorithm,a rainfall inversion algorithm based on social surveillance video is proposed.Firstly,the rainfall classification network is adopted to remove the no-rain video.Secondly,the foreground information of rainfall videos is extracted by using the alternating direction method of multipliers (ADMM),and the region of interest (ROI) is chosen by semantic segmentation and background subtraction methods.Thirdly,a Gaussian mixture model (GMM) characterized by gray-scale change and saturation features is constructed to choose the raindrops in ROI.Finally,the raindrop size is calculated according to the perspective imaging relations,and the rainfall is inverted through the meteorological Gamma model.The experimental results show that the rainfall classification accuracy of the method reaches 91.3% in the multi-class weather dataset (MWD) and 77.0% in the real dataset,and the rainfall estimation results are more accurate compared with the existing methods.