空-频域联合投票的交通视频阴影去除方法  

Shadow Removal of Traffic Surveillance Video by Joint Voting in Spatial-Frequency Domain

作　　者：宋传鸣[1] 洪旭 王相海[1] SONG Chuan-ming;HONG Xu;WANG Xiang-hai(School of Computer and Information Technology,Liaoning Normal University,Dalian,Liaoning 116029,China)

机构地区：[1]辽宁师范大学计算机与信息技术学院,辽宁大连116029

出　　处：《计算机科学》2020年第5期129-136,共8页Computer Science

基　　金：大连市高层次人才创新支持计划项目(2015R069)。

摘　　要：交通场景中的静止或运动阴影往往会降低车辆目标跟踪的精度,因此有效地去除阴影是交通监控视频处理的重要环节之一。然而,目前尚无一种能够同时发掘阴影的空间域和频率域特性且抵抗静止和运动阴影干扰的阴影去除方法。为此,提出了一种基于空-频域联合投票策略的交通视频阴影去除方法。首先,将视频帧从RGB颜色空间转换到HSV颜色空间,再进行非下采样剪切波变换;其次,假设变换系数服从高斯分布,采用变换系数的均值和标准差计算每个尺度的加权掩码;然后,根据多尺度变换系数的零树分布特性,利用粗尺度的加权掩码校正细尺度的加权掩码,将各个尺度、各个颜色通道的加权掩码进行线性组合后得到一个公共掩码,再采用基于最小二乘法拟合的最大熵方法计算自适应分割阈值,对公共掩码进行二值化;最后,联合频率域加权掩码、S通道和V通道的掩码进行投票,进而确定去除阴影后的运动车辆区域。实验结果表明,该算法可有效去除交通监控视频中的静态/运动阴影,抑制阴影的干扰,将传统Meanshift算法的输出车辆轨迹与真实轨迹间的平均欧氏距离缩小95%,且未出现目标丢失的现象,增强了智能分析算法的鲁棒性。研究结果说明,该算法有效联合交通监控视频的空间域和频率域表示,充分发掘了运动车辆区域与阴影区域之间的纹理特性和颜色特性差异,有利于获得更精确的阴影去除结果,进而提高车辆目标跟踪的精度。The static or moving shadows in traffic scenes tend to reduce the accuracy of vehicle target tracking.Thus,it is an important step to effectively remove the shadows in the processing of traffic surveillance videos.However,there hardly is an efficient shadow removal method yet at present,which resists both static and moving shadows by simultaneously exploring the spatial and frequency characteristics of shadows.Under such a circumstance,this study proposed a shadow removal method for traffic video by using a joint voting strategy in spatial-frequency domain.The surveillance video is converted from RGB space into HSV space and then performed non-subsampled shearlet transform(NSST).Assuming that NSST coefficients follows the Gaussian distribution,the mean and standard deviation of coefficients is used to compute the weighted mask for each scale.Subsequently,the weighted mask at coarse scale is employed to adjust the mask at fine scale,according to the zerotree characteristics of multiscale coefficients.The weighted masks of different scales and color channels are thus linearly combined to form a unified mask,which is then binarized by an adaptive threshold calculated by the maximum entropy segmentation based on the least square method.Finally,the moving vehicle area after shadow removal is determined by a joint voting strategy by using the weighted frequency-domain mask,the S-channel mask and the V-channel mask respectively.Experimental results show that the proposed algorithm can effectively remove the static and moving shadows in traffic surveillance video.It reduces the average Euclidean distance by 95%between the ideal trajectory and the output vehicle trajectory of traditional mean shift algorithm,suppressing the interfe-rence of shadows.Meanwhile,the proposed algorithm enhances the robustness of the intelligent analysis and avoids the phenomenon of losing the target.Our research indicates that it is conducive to obtaining more accurate shadow removal result to effectively combine the representation of traffic

关 键 词：交通监控视频 阴影去除 非下采样剪切波 联合投票 多尺度加权掩码 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]