%0 Journal Article
%T 改进的混合动静态背景的分割方法<br>An Improved Subtraction Algorithm of Backgrounds with Stationary and Non？？Stationary Scenes
%A 刘天良
%A 郑海红
%A 罗圣美
%A 梁玮
%A 邓硕
%J 西安交通大学学报
%D 2015
%R 10.7652/xjtuxb201502005
%X 针对动静态背景场景下背景分割虚警率高的问题,提出了一种基于块直方图特征的Zivkovic混合高斯模型改进算法―BHZ？？MoG。该算法设计了图像的块观测向量,并根据块观测向量的统计规律将图像块分类为动态、半动态、静态块,由此给出了结合块观测向量与块分类的块直方图特征提取算法,同时结合块直方图特征与Zivkovic混合高斯模型对不同类型的块分别进行背景分割与模型更新。实验结果表明,相较于Zivkovic混合高斯模型,BHZ？？MoG算法能够在保证查全率不变的情况下,有效提高背景分割结果的查准率;Zivkovic混合高斯模型及BHZ？？MoG的最大F1分数分别为0.758和0.790,说明了BHZ？？MoG算法可以获得较佳的前、背景分割效果。另外,BHZ？？MoG算法还可有效降低Zivkovic混合高斯模型在动态背景下的虚警率。<br>An improved algorithm―BHZ？？MoG of Zivkovic？？mixture of Gaussians (Z？？MoG) based on block histogram feature is proposed to solve the problem that the background subtraction has a high false alarm rate in the mixture of stationary and non？？stationary scenes. Observation vectors for image blocks are designed and blocks are classified into static, dynamic and half？？dynamic blocks according to the statistical regularities of the observation vectors. A method that combines the observation vector and the classified information of a block is presented to extract block histogram feature. Block background models are constructed and updated from the combination of Z？？MoG and histogram features. The BHZ？？MoG can effectively reduce the high false alarm rate of Z？？MoG under dynamic backgrounds. Experimental results show that the precision of the BHZ？？MoG is higher than that of Z？？MoG while the recall keeps the same. The maximal F1？？scores of the Z？？MoG is 0.758 and that of the BHZ？？MoG is 0.790, and it shows that the proposed algorithm can provide better subtraction results
%K 背景分割
%K 图像块分类
%K 颜色直方图特征
%K 混合高斯模型&lt
%K br&gt
%K background subtraction
%K image block classification
%K color histogram feature
%K mixture of Gaussians
%U http://zkxb.xjtu.edu.cn/oa/DArticle.aspx?type=view&id=201502005