多特征融合及最小均方误差优化的阴影检测  

Shadow detection with multi-feature fusion and MMSE optimization

作　　者：张涵[1] 闫怀平[1] 张展[2] Zhang Han;Yan Huaiping;Zhang Zhan(School of Computer Science and Information Engineering,Anyang Institute of Technology,Anyang 455000,China;School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]安阳工学院计算机科学与信息工程,河南安阳455000 [2]河南理工大学电气学院,河南焦作454000

出　　处：《电子技术应用》2018年第10期153-157,共5页Application of Electronic Technique

摘　　要：为降低阴影对运动目标检测结果的干扰,提出了一种阴影检测方法,作为运动侦测方法的后处理步骤。在运动侦测检测到的目标列表的基础上,该方法针对R、G、B 3个颜色通道提取相邻帧之间像素点的亮度、对比度和结构特征,融合这3类特征生成相似度度量,并依据最小均方误差准则设计目标函数,通过最优化方法求解最佳的像素点分割阈值,检测并消除运动侦测目标中的阴影像素点。阴影检测实验在Changedetection.net数据集的shadow数据子集进行。实验结果表明,该方法的阴影检测率高,检测耗时少。In order to reduce the interference of shadow to detection results of moving targets,a shadow detection method is proposed,which can be made as the post-processing step of motion detection methods.On the basis of the target list detected by motion detection methods,this method extracts the brightness,contrast and structural features of the pixels between adjacent frames for the three color channels R,G and B,and fuses the three types of features to generate the similarity measure.And the objective function is designed according to the MMSE(minimum mean square error)criterion.The optimal pixel segmentation thresholds are obtained by the optimal solution,to detect and eliminate the shadow pixels in the motion detection targets.The shadow detection experiments are carried out on the shadow subset of Changedetection.net dataset.The experimental results show that this method has high detection rate and low detection time.

关 键 词：阴影检测 运动侦测 最小均方误差 最优化 多特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]