[1]王 丹,刘 怀.基于改进混合高斯模型的背景提取与更新[J].南京师范大学学报(工程技术版),2015,15(02):060.
 Wang Dan,Liu Huai.Background Extraction and Updating Based on Improved Gaussion Mixture Model[J].Journal of Nanjing Normal University(Engineering and Technology),2015,15(02):060.
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基于改进混合高斯模型的背景提取与更新
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
15卷
期数:
2015年02期
页码:
060
栏目:
计算机与信息工程
出版日期:
2015-06-20

文章信息/Info

Title:
Background Extraction and Updating Based on Improved Gaussion Mixture Model
作者:
王 丹刘 怀
南京师范大学电气工程与自动化工程学院,江苏 南京 210042
Author(s):
Wang DanLiu Huai
School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210042,China
关键词:
混合高斯模型背景提取更新率
Keywords:
gaussion mixture modelbackground extractionupdating ratios
分类号:
TP391
文献标志码:
A
摘要:
针对序列图像背景提取不能正确地处理场景突变、实时性差等问题,本文提出了改变更新率的背景提取算法. 首先,对传统混合高斯模型进行了简化,其次对像素点划分区域,在不同区域采用不同的背景更新率,有选择性地进行背景更新. 实验结果表明,该方法提高了提取背景模型的实时性和精度.
Abstract:
The new algorithm that can alter the background updating ratio is presented in this paper in order to adapt the case that background may change and decrease its execution time. Firstly,the algorithm simplifies the traditional Gaussion mixture model. Then it partitions the image and different updating ratios are employed for different areas to update the background. In addition,the algorithm can also change the updating ratio when the background changes suddenly. The experiment shows that the algorithm presented in this paper can extract the background from sequence images quickly and accurately and it can also adapt to the case that the background change suddenly.

参考文献/References:

[1] 张水发,张文生,丁欢,等. 融合光流速度与背景建模的目标检测方法[J]. 中国图象图形学报,2011,16(2):236-243.
Zhang Shuifa,Zhang Wensheng,Ding Huan,et al. Background modeling and object detecting based on optical flow velocity field[J]. Journal of Image and Graphics,2011,16(2):236-243.
[2]许敬,张合,张祥金. 基于帧间差分和光流法的红外图像运动检测[J]. 计算机仿真,2012,29(6):248-252.
Xu Jing,Zhang He,Zhang Xiangjin. IR Motive Detection Using Image Subtraction and Optical Flow[J]. Computer Simulation,2012,29(6):248-252.
[3]王小平,张丽杰,常佶. 基于单高斯背景模型运动目标检测方法的改进[J]. 计算机工程应用,2009,45(21):118-120.
Wang Xiaoping,Zhang Lijie,Chang Ji. Improved method of moving objects detection based on single-gaussian background model[J]. Computer Engineering and Applications,2009,45(21):118-120.
[4]钟珞,刘剑. 基于混合高斯和均值滤波法的运动检测方法[J]. 武汉理工大学学报:信息与管理工程版,2010,32(5):691-693.
Zhong Luo,Liu Jian. Object detecting based on Gaussion mixture model and mean filtering [J]. Journal of WUST:Information and Management Engineering,2010,32(5):691-693.
[5]李伟,陈临强,殷伟良. 基于自适应学习率的背景建模方法[J]. 计算机工程,2011,37(15):187-189.
Li Wei,Chen Linqiang,Yin Weiliang. Background Modeling Approach Based on Self-adaptive Learning Rate[J]. Computer Engineering,2011,37(15):187-189.
[6]朱齐丹,李科,张智,等. 改进的混合高斯自适应背景模型[J]. 哈尔滨工程大学学报,2010,31(1):1 348-1 353.
Zhu Qidan,Li Ke,Zhang Zhi,et al. Adaptive background modeling based on improved Gaussion mixture model[J]. Journal of Harbin Engineering University,2010,31(1):1 348-1 352.
[7]刘静,王玲. 混合高斯模型背景法的一种改进算法[J]. 计算机工程与应用,2010,26(43):168-170.
Liu Jing,Wang Ling. Improved algorithm of Gaussion mixture model for background subtraction[J]. Computer Engineering and Applications,2010,26(43):168-170.
[8]李明之,马志强,单勇,等. 复杂条件下高斯混合模型的自适应背景更新[J]. 计算机应用,2011,30(7):1 831-1 834.
Li Mingzhi,Ma Zhiqiang,Shan Yong,et al. Adaptive background update based on Gaussion mixture model under complex condition[J]. Journal of Computer Applications,2011,30(7):1 831-1 834.

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备注/Memo

备注/Memo:
收稿日期:2014-10-14.
基金项目:教育部“留学回国人员科研启动基金”(2014)1685号.
通讯联系人:刘怀,副教授,研究方向:实时控制系统及数字图像处理. E-mail:liuhuai@njnu.edu.cn
更新日期/Last Update: 2015-06-20