基于改进混合高斯模型的运动目标检测算法  

Moving Object Detection Algorithm Based on Improved Gaussian Mixture Model

作　　者：孙毅刚[1] 李鸿[1] 张红颖[1] 

机构地区：[1]中国民航大学航空自动化学院,天津300300

出　　处：《计算机工程》2012年第18期166-170,共5页Computer Engineering

基　　金：国家自然科学基金委员会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专项基金资助项目(ZXH2009B004);中国民航局科技基金资助项目(MHRD201002)

摘　　要：在运动目标停滞的情况下,传统的混合高斯模型会将运动目标误判为背景,导致目标漏检。为此,提出一种基于改进混合高斯模型的目标检测算法。引入背景学习参数,结合前一帧的检测结果自适应地更新背景,从而提取完整的运动目标。利用像素的八连通区域信息抑制噪声,提高算法在复杂环境中的稳定性。实验结果表明,与传统检测方法相比,该算法能够在复杂环境中准确地检测出短暂停滞的运动目标。Moving objects can be converted into background ones by Gaussian Mixture Model（GMM） when they might be staying still in the scene for an uncertain time.Therefore,a new method based on improved GMM is proposed.To obtain complete objects,a background learning parameter is introduced to update the model according to the detection result in previous frame.Moreover,the information of 8-adjacent connection area is utilized to suppress noises and improve its stability in the complex environment.Several experiments are implemented and the results demonstrate its effectiveness in detecting the moving objects which stay briefly in the complicated condition.

关 键 词：混合高斯模型 运动目标检测 背景模型 噪声抑制 连通区域 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]