中国石油大学学报（自然科学版）2012，Vol.36Issue(5)：175-178,183,5.DOI:10.3969/j.issn.1673-5005.2012.05.033
一种基于混合高斯的双空间自适应背景建模方法
A double-subspace adaptive background modeling method based on Gaussian mixture model
摘要
Abstract
In order to tackle problems that the moving object slows down or stops for a while, and the background changes suddenly when segregating the foreground from background;' inspired by the human learning process, a double-subspace a-daptive background modeling method based on Gaussian mixture model(GMM) was proposed. A memory space is introduced into the traditional GMM-based background modeling for storing the past background models. The learning rates for updating the distributions in the two spaces are different. In GMM space, the learning rate is updated with the contribution of the distribution to the scene, which aims to handle problems that the object moves slowly or stops temporarily. While in the memory space, a fixed learning rate is used in order to improve the adaptability to sudden background changes. The experimental results demonstrate the superiority of the proposed method.关键词
背景建模/混合高斯模型/运动目标分割/背景减除/背景突变Key words
background modeling/ Gaussian mixture model (GMM) / moving object segmentation/ background subtraction/sudden background changes分类
信息技术与安全科学引用本文复制引用
齐玉娟,王延江,索鹏..一种基于混合高斯的双空间自适应背景建模方法[J].中国石油大学学报（自然科学版）,2012,36(5):175-178,183,5.基金项目
国家自然科学基金项目(60873163,61271407) （60873163,61271407）
中央高校基本科研业务费专项资金资助项目(27R1105019A) （27R1105019A）
