[1]谢维波,刘文,夏远祥,等.双背景模型的快速鲁棒前景检测算法[J].华侨大学学报(自然科学版),2017,38(4):550-555.[doi:10.11830/ISSN.1000-5013.201704020]
 XIE Weibo,LIU Wen,XIA Yuanxiang,et al.Fast and Robust Foreground Detection Algorithm Based on Double Background Model[J].Journal of Huaqiao University(Natural Science),2017,38(4):550-555.[doi:10.11830/ISSN.1000-5013.201704020]
点击复制

双背景模型的快速鲁棒前景检测算法()
分享到:

《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第38卷
期数:
2017年第4期
页码:
550-555
栏目:
出版日期:
2017-07-10

文章信息/Info

Title:
Fast and Robust Foreground Detection Algorithm Based on Double Background Model
文章编号:
1000-5013(2017)04-0550-06
作者:
谢维波1 刘文1 夏远祥1 李雪芬2
1. 华侨大学 计算机科学与技术学院, 福建 厦门 361021;2. 华侨大学 科学技术研究处, 福建 厦门 361021
Author(s):
XIE Weibo1 LIU Wen1 XIA Yuanxiang1 LI Xuefen2
1. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China; 2. Science and Technology Research Department, Huaqiao University, Xiamen 361021, China
关键词:
光照鲁棒 前景检测 双背景模型 视频信息 时间感知信息
Keywords:
illumination robust foreground detection double background model video information time perception information
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201704020
文献标志码:
A
摘要:
针对前景检测中的光照变化问题,提出一种基于双背景模型的快速鲁棒前景检测算法.通过建立简单的快慢双背景模型,提高前景检测的效率.结合视频时间感知信息和光照补偿措施,增强算法对光照变化的鲁棒性,提高前景检测精度.在具有光照变化的公开数据集上进行测试,实验结果表明:所提出的算法不仅对光照变化有较强的鲁棒性,同时,具有极快的处理速度.
Abstract:
In order to solve the problem of illumination changes in foreground detection, a fast and robust foreground detection algorithm based on double background model was proposed in this paper. The efficiency can be improved by establishing a simple double background model with fast and slow update rate; the robustness with illumination variations can be enhanced by combining with video time perception information and illumination compensation measures, and hence improving the precision of the foreground detection. Experiments were performed on several challenging sequences with illumination variations in the benchmark evaluation, and the results show that the proposed algorithm not only owns good robustness with changing of illumination, but also has very fast process speed.

参考文献/References:

[1] BOUWMANS T,PORIKLI F,HOFERLIN B,et al.Background modeling and foreground detection for video surveillance[M].Boca Raton:CRC Press,2014:3-8.
[2] SOBRAL A,VACAVANT A.A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos[J].Computer Vision and Image Understanding,2014,122:4-21.
[3] STAUFFER C,GRIMSON W E L.Adaptive background mixture models for real-time tracking[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Fort Collins:IEEE Press,1999:246-252.
[4] ELGAMMAL A,HARWOOD D,DAVIS L.Non-parametric model for background subtraction[C]//European Conference on Computer Vision.Dublin:Springer Berlin Heidelberg,2000:751-767.
[5] KIM K,CHALIDABHONGSE T H,HARWOOD D,et al.Real-time foreground-background segmentation using codebook model[J].Real-Time Imaging,2005,11(3):172-185.
[6] BARNICH O,VAN DROOGENBROECK M.ViBe: A universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709-1724.
[7] VOSTERS L,SHAN Caifeng,GRITTI T.Real-time robust background subtraction under rapidly changing illumination conditions[J].Image and Vision Computing,2012,30(12):1004-1015.
[8] SAJID H,CHEUNG S C S.Background subtraction under sudden illumination change[C]//2014 IEEE 16th International Workshop on Multimedia Signal Processing.Jakarta:IEEE Press,2014:1-6.
[9] LI Xuli,ZHANG Chao,ZHANG Duo.Abandoned objects detection using double illumination invariant foreground masks[J].IEEE Journal of Selected Topics in Signal Processing,2010,2(4):582-596.
[10] GRUENWEDEL S,PETROVIC N I,JOVANOV L,et al.Efficient foreground detection for real-time surveillance applications[J].Electronics Letters,2013,49(18):1143-1145.
[11] MAHMOUDPOUR S,KIM M.Robust foreground detection in sudden illumination change[J].Electronics Letters,2016,52(6):441-443.
[12] 国际电联无线电通信全会.ITU-R BT.1788 建议书: 对多媒体应用中视频质量的主观评估方法[S].ITU:ITU-R 102/6 号研究课题,2007:1-13.
[13] 杨涛,李静,潘泉,等.一种基于多层背景模型的前景检测算法[J].中国图象图形学报,2008,13(7):1303-1308.
[14] YAO Jian,ODOBEZ J M.Multi-layer background subtraction based on color and texture[C]//IEEE Conference on Computer Vision and Pattern Recognition.Minneapolis:IEEE Press,2007:1-8.
[15] MADDALENA L,PETROSINO A.A self-organizing approach to background subtraction for visual surveillance applications[J].IEEE Transactions on Image Processing,2008,17(7):1168-1177.

备注/Memo

备注/Memo:
收稿日期: 2016-05-26
通信作者: 谢维波(1964-),男,教授,博士,主要从事信号处理、视频分析和嵌入式系统的研究.E-mail:xwblxf@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61271383); 华侨大学研究生科研创新能力培育计划项目(1400214010)
更新日期/Last Update: 2017-07-20