[1]张夏清,茅耀斌. 一种改进的ViBe背景提取算法[J].计算机技术与发展,2016,26(07):36-39.
　ZHANG Xia-qing,MAO Yao-bin. An Improved ViBe Background Generation Method[J].,2016,26(07):36-39.
点击复制

 一种改进的ViBe背景提取算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年07期
页码:
36-39
栏目:
智能、算法、系统工程
出版日期:
2016-07-10

文章信息/Info

Title:
 An Improved ViBe Background Generation Method
文章编号:
1673-629X（2016)07-0036-04
作者:
 张夏清; 茅耀斌
 南京理工大学 自动化系
Author(s):
 ZHANG Xia-qing; MAO Yao-bin
关键词:
 ; ViBe算法; 背景生成; 目标检测; 颜色直方图 ; 均值漂移
Keywords:
 ; ViBe algorithm; background generation; object detection; color histogram; Mean Shift
分类号:
TP301.6
文献标志码:
A
摘要:
 背景生成算法通过提取视频场景背景的方法，检测出运动的前景对象，在智能视频监控应用中具有重要的意义。针对ViBe背景提取算法，文中提出一种改进方法。在样本采集方面，提出了利用高斯分布在像素点的邻域中进行采样的方法；在样本更新方面，提出自适应概率的随机子采样方法；在背景生成方面，构建样本集的像素直方图，提出了通过颜色直方图和Mean Shift迭代进行背景生成的方法，并结合时空域的像素信息进行前景检测。具体描述了改进的ViBe背景生成算法，并通过实验比较了改进算法与ViBe背景提取算法的效果。结果表明，改进的ViBe算法在背景生成和前景提取方面均达到了更好的效果。
Abstract:
 Background generation and foreground detection play a significant role in the field of intelligent video surveillance. By extrac-ting the background from video scenes,the algorithm can detect the movement of the foreground objects. An improved background gener-ation method based on ViBe background extraction is proposed. In terms of sample collection,samples are taken in the neighborhood of every pixel according to a Gaussian distribution. Regarding sample updating,an adaptive probability of random sub-sampling is presen-ted. When it comes to background generation,by constructing a color histogram of the sample pixels,a background generation method is put forward based on color histograms and Mean Shift iteration. And the foreground is detected with the pixel information in both time and space domain. It specifically describes an improved ViBe background generation method in this paper,which is compared with the o-riginal algorithm by means of experiments. The results show that the proposed algorithm is more effective in both background generation and foreground extraction.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(07):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(07):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(07):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(07):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(07):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(07):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(07):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(07):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(07):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(07):47.

更新日期/Last Update: 2016-09-28