基于元认知模型的智能混合高斯背景建模  

Metacognitive Model-based Intelligent Gaussian Mixture Background Modeling

作　　者：陈真[1] 王钊[1] 

机构地区：[1]中国石油大学(华东)电子信息工程系,青岛266580

出　　处：《计算机系统应用》2013年第9期180-184,159,共6页Computer Systems & Applications

基　　金：山东省优秀中青年科学家科研奖励基金(BS2011DX040);中央高校基本科研业务费专项资金(11CX04045A)

摘　　要：传统混合高斯背景模型(Gaussian mixture model,GMM)不能快速适应动态场景中背景发生突变的情况.本文提出一种基于元认知模型的智能混合高斯背景建模方法,每个输入像素经过元认知监控成分刺激元认知体验成分以提取成功(或失败)的意识进行认知,根据提取的意识及时向元认知知识成分传输新的认知知识或直接提取元认知知识成分,并作出决策信息.该方法能够对背景模型产生认知,当背景突变为认知过的背景时,可以快速适应并能更准确地描述复杂场景中的真实背景.Traditional Gaussian Mixture Model （GMM） can＇t adapt quickly to the sudden changes in the dynamic scene. A Metacognitive Model-based Intelligent Gaussian Mixture background modeling method is proposed. For each pixel, decision making must pass the three elements which are called as Metacognitive Monitoring （MM） element, Metacognitive Cognitive Knowledge （MCK） element and Metacognitive Experience （ME） element. MM element monitors the modeling scheme, stimulates to get ME element, and extracts cognitive knowledge from MCK element. MCK element is composed of the background models cognized ever. ME element is composed of the experiences from success or failure matching. In the complex scene, the proposed method can intelligently cognize background, quickly adapt to the sudden changes of backgrounds cognized ever and describe real background more accurately.

关 键 词：混合高斯模型 元认知模型 背景建模 目标检测 背景减除 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]