基于混合高斯模型的阴影去除算法  

Shadow removal algorithm based on Gaussian mixture model

作　　者：张红颖[1] 李鸿[1] 孙毅刚[1] 

机构地区：[1]中国民航大学航空自动化学院,天津300300

出　　处：《计算机应用》2013年第1期31-34,共4页journal of Computer Applications

基　　金：国家自然科学基金委与中国民用航空局联合基金资助项目(60979005);中央高校基本科研业务费中国民航大学专项(ZXH2009B004);中国民航局科技基金资助项目(MHRD201002);天津市自然科学基金青年基金资助项目(12JCQNJC00600)

摘　　要：阴影去除是智能视频领域中运动目标识别的一项重要内容,其结果直接影响目标识别的准确性。针对当前基于纹理特征的阴影去除算法的不足,提出一种结合YCbCr颜色空间和混合高斯模型(GMM)的阴影去除算法。首先利用混合高斯模型提取出运动区域;然后通过分析运动区域的前景和背景在YCbCr颜色空间的差值统计特性,建立混合高斯阴影模型;最后根据高斯分布的概率分布规律,得到阴影分布特性,从而实现对阴影的去除。对于实验中的序列图像,所提算法有70%以上的阴影检测率。实验结果表明,所提方法能够在不同的场合快速有效地去除阴影,准确地提取运动目标。Shadow removal is one of the most important parts of moving object detection in the field of intelligent video since the shadow definitely affects the recognition result. In terms of the disadvantage of shadow removal methods utilizing texture, a new algorithm based on Gaussian Mixture Mode/ （GMM） and YCbCr color space was proposed. Firstly, moving regions were detected using GMM. Secondly, the Gaussian mixture shadow model was built through analyzing the color statistics of the difference between the foreground and background of the moving regions in YCbCr color space. Lastly, the threshold value of the shadow was obtained according to the Gaussian probability distribution in YCbCr color space. More than 70 percent of shadow pixels in sequence images of the experiments could be detected by the algorithm accurately. The experimental resuhs show that the proposed algorithm is efficient and robust in object extraction and shadow detection under different scenes.

关 键 词：阴影模型 YCBCR颜色空间 混合高斯模型 阴影去除 颜色统计特性 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]