基于判别模型的视频前景／阴影自动分割算法  

Video Foreground and Shadow Automatic Segmentation Based on Discriminative Model

作　　者：褚一平[1,2] 陈勤[1] 黄叶珏[3] 张三元[2] 

机构地区：[1]杭州电子科技大学计算机科学学院,杭州310018 [2]浙江大学计算机科学与技术学院,杭州310027 [3]浙江工业职业技术学院计算机科学系,绍兴312000

出　　处：《模式识别与人工智能》2008年第6期849-855,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60473106,No.60333010);国家教育部博士点基金项目(No.20060335114);浙江省教育厅项目(No.G20030433)资助

摘　　要：活动阴影是影响视频目标分割准确性的重要因素,有效检测与消除活动阴影是视频分割的一大难题.本文提出一种基于判别模型的前景/阴影自动分割算法.它能在室内户外多种环境中对活动阴影进行检测和消除.算法在像素级别上对背景、阴影以及前景进行建模,利用二维条件随机场对这些分布模型进行约束,通过概率图模型推断算法求出全局最优的分割结果.在实验中采用各种环境的视频数据对本文算法的有效性进行测试,并与其他分割算法的结果进行比较,证明本文算法的误分率较低.Moving cast shadows are factors affecting segmentation quality. Efficient shadow detection and removal is a difficult problem in video segmentation. A method based on discriminative model for video foreground and shadow segmentation is proposed. It has capability of shadow detection and removal under different scenes. The proposed algorithm models background, shadows and foreground at pixel levels. These models are constrained by using 2-dimensional conditional random fields. Inference algorithm of probabilistic graphical models is adopted to obtain globally optimal segmentation results. The experimental results demonstrate the validity of the proposed algorithm, and the results are compared with other algorithms by using outdoor and indoor video data.

关 键 词：视频分割 活动阴影模型 二维条件随机场 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]