2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

动态场景红外图像的压缩感知域高斯混合背景建模

王传云 秦世引

王传云, 秦世引. 动态场景红外图像的压缩感知域高斯混合背景建模. 自动化学报, 2018, 44(7): 1212-1226. doi: 10.16383/j.aas.2017.c170061
引用本文: 王传云, 秦世引. 动态场景红外图像的压缩感知域高斯混合背景建模. 自动化学报, 2018, 44(7): 1212-1226. doi: 10.16383/j.aas.2017.c170061
WANG Chuan-Yun, QIN Shi-Yin. Background Modeling of Infrared Image in Dynamic Scene With Gaussian Mixture Model in Compressed Sensing Domain. ACTA AUTOMATICA SINICA, 2018, 44(7): 1212-1226. doi: 10.16383/j.aas.2017.c170061
Citation: WANG Chuan-Yun, QIN Shi-Yin. Background Modeling of Infrared Image in Dynamic Scene With Gaussian Mixture Model in Compressed Sensing Domain. ACTA AUTOMATICA SINICA, 2018, 44(7): 1212-1226. doi: 10.16383/j.aas.2017.c170061

动态场景红外图像的压缩感知域高斯混合背景建模

doi: 10.16383/j.aas.2017.c170061
基金项目: 

辽宁省教育厅科研项目 L201726

沈阳市科技计划项目 18-013-0-24

国家自然科学基金 61703287

北京市科技计划项目 D16110400130000-D161100001316001

国家自然科学基金 61731001

国家自然科学基金 U1435220

沈阳航空航天大学博士启动基金 17YB16

详细信息
    作者简介:

    王传云 北京航空航天大学自动化科学与电气工程学院博士研究生.沈阳航空航天大学计算机学院讲师.2009年获得沈阳航空航天大学计算机应用技术硕士学位.主要研究方向为模式识别, 物联网.E-mail:wangcy0301@buaa.edu.cn

    通讯作者:

    秦世引 北京航空航天大学自动化科学与电气工程学院教授.1990年获得浙江大学工业控制工程与智能自动化博士学位.主要研究方向为图像处理, 模式识别, 智能优化控制.本文通信作者.E-mail:qsy@buaa.edu.cn

Background Modeling of Infrared Image in Dynamic Scene With Gaussian Mixture Model in Compressed Sensing Domain

Funds: 

Scientific Research Project of Liaoning Educational Department L201726

Shenyang Science and Technology Project 18-013-0-24

National Natural Science Foundation of China 61703287

Beijing Science and Technology Project D16110400130000-D161100001316001

National Natural Science Foundation of China 61731001

National Natural Science Foundation of China U1435220

Doctoral Scientific Research Foundation of Shenyang Aerospace University 17YB16

More Information
    Author Bio:

    Ph. D. candidate at the School of Automation Science and Electrical Engineering, Beihang University, lecturer at the School of Computer Science, Shenyang Aerospace University. He received his master degree in computer application technology from Shenyang Aerospace University in 2009. His research interest covers pattern recognition and internet of things

    Corresponding author: QIN Shi-Yin Professor at the School of Automation Science and Electrical Engineering, Beihang University. He received his Ph. D. degree in industrial control engineering and intelligent automation from Zhejiang University in 1990. His research interest covers image processing, pattern recognition, and intelligent optimizing controls. Corresponding author of this paper
  • 摘要: 针对动态场景下红外图像的背景模型构建问题,提出一种基于压缩感知(Compressed sensing,CS)域高斯混合模型(Gaussian mixture model,GMM)的背景建模方法.该方法不是对图像中的每个像素建立高斯混合模型,而是对图像局部区域的压缩感知测量值建立高斯混合模型.1)通过提取红外图像轮廓的角点特征,估计相邻帧图像间的相对运动参数以对图像进行校正与配准;2)将每帧图像网格化为适当数目的局部子图,利用序列图像构建每个局部子图的压缩感知域高斯混合背景模型;3)采用子空间学习训练稀疏字典,通过子空间追踪对可能含有目标的局部子图进行选择性稀疏重构;4)通过背景减除实现前景目标检测.以红外图像数据集CDnet2014和VIVID PETS2005进行实验验证,结果表明:该方法能建立有效的动态场景红外图像背景模型,对成像过程中所受到的场景动态变化、背景扰动等具有较强的鲁棒性,其召回率、精确率、F-measure等性能指标及处理速度较之于同类算法具有明显优势.
    1)  本文责任编委 胡清华
  • 图  1  局部图像的压缩感知域高斯混合背景建模过程示意图

    Fig.  1  Diagram of local background modeling with Gaussian mixture model in compressed sensing domain

    图  2  通过子空间学习从训练样本生成的稀疏字典示例

    Fig.  2  An example of sparse dictionary generated from training samples by subspace learning

    图  3  原红外图像及提取角点特征的图像轮廓

    Fig.  3  Original infrared image and contour with corners

    图  4  利用RANSAC算法匹配相邻两帧图像的角点特征

    Fig.  4  Corner features matching between two frames by using RANSAC algorithm

    图  5  基于双线性灰度插值的畸变图像几何校正

    Fig.  5  Geometric correction of distortion image based on bilinear interpolation

    图  6  动态场景红外图像的压缩感知域高斯混合背景建模及目标检测流程图

    Fig.  6  Flow chart of background modeling with Gaussian mixture model in compressed sensing domain and target detection of infrared image in dynamic scene

    图  7  固定场景图像序列下各算法的平均召回率和精确率

    Fig.  7  The average recall and precision of different algorithms in fixed scene image sequences

    图  8  Park场景代表性图像及各算法得到的前景掩膜图像

    Fig.  8  Images of park scene and foreground masks obtained from different algorithms

    图  9  Lakeside场景代表性图像及各算法得到的前景掩膜图像

    Fig.  9  Images of lakeside scene and foreground masks obtained from different algorithms

    图  10  动态场景图像序列下各算法的平均召回率和精确率

    Fig.  10  The average recall and precision of different algorithms in dynamic scene image sequences

    图  11  Pktest01场景代表性图像及各算法得到的前景掩膜图像

    Fig.  11  Images of Pktest01 scene and foreground masks obtained from different algorithms

    图  12  Pktest03场景代表性图像及各算法得到的前景掩膜图像

    Fig.  12  Images of Pktest03 scene and foreground masks obtained from different algorithms

    表  1  固定场景图像序列下各算法的F-measure指标

    Table  1  The F-measure index of different algorithms in fixed scene image sequences

    GMM KDE Codebook ViBe GRASTA DECOLOR Ours
    CDnet2014 park 0.6429 0.3761 0.3379 0.5335 0.4645 0.8098 0.6607
    CDnet2014 lakeside 0.2561 0.0185 0.1943 0.2 0.0238 0.224 0.7848
    下载: 导出CSV

    表  2  处理固定场景中一帧红外图像的平均时间消耗(s)

    Table  2  The average time consumption of each infrared image in fixed scenes (s)

    压缩感知 模型构建 稀疏重构 背景减除
    CDnet2014 park 0.0065 0.1352 0.4938 0.0003
    CDnet2014 lakeside 0.0043 0.0827 0.5594 0.0003
    下载: 导出CSV

    表  3  动态场景图像序列下各算法的F-measure指标

    Table  3  The F-measure index of different algorithms in dynamic scene image sequences

    GMM KDE Codebook ViBe GRASTA DECOLOR Ours
    PETS2005 pktest01 0.0089 0.0052 0.0040 0.0086 0.0125 0.3927 0.3369
    PETS2005 pktest03 0.0099 0.0086 0.0062 0.0123 0.0047 0.1929 0.2198
    下载: 导出CSV

    表  4  处理动态场景中一帧红外图像的平均时间消耗(s)

    Table  4  The average time consumption of each infrared image in dynamic scenes (s)

    图像校正与配准 背景建模与重构
    DECOLOR Ours DECOLOR Ours
    PETS2005 pktest01 0.6610 1.4632 0.6053 0.5509
    PETS2005 pktest03 0.7608 1.1273 0.6942 0.6454
    下载: 导出CSV
  • [1] Cao Y, Liu R M, Yang J. Small target detection using two-dimensional least mean square (TDLMS) filter based on neighborhood analysis. International Journal of Infrared and Millimeter Waves, 2008, 29(2):188-200 doi: 10.1007/s10762-007-9313-x
    [2] Bae T W, Kim Y C, Ahn S H, Sohng K I. An efficient two-dimensional least mean square (TDLMS) based on block statistics for small target detection. Journal of Infrared, Millimeter, and Terahertz Waves, 2009, 30(10):1092-1101 doi: 10.1007/s10762-009-9530-6
    [3] Kim S. Double layered-background removal filter for detecting small infrared targets in heterogenous backgrounds. Journal of Infrared, Millimeter, and Terahertz Waves, 2011, 32(1):79-101 doi: 10.1007/s10762-010-9742-9
    [4] Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, Colorado, USA: IEEE, 1999, 2: 252
    [5] Lee D S. Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5):827-832 doi: 10.1109/TPAMI.2005.102
    [6] Haines T S F, Xiang T. Background subtraction with dirichlet process mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(4):670-683 doi: 10.1109/TPAMI.2013.239
    [7] Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. In: Proceedings of the 6th European Conference on Computer Vision. Berlin, Heidelberg, Germany: Springer, 2000. 751-767
    [8] Kim K, Chalidabhongse T H, Harwood D, Davis L. Real-time foreground-background segmentation using codebook model. Real-Time Imaging, 2005, 11(3):172-185 doi: 10.1016/j.rti.2004.12.004
    [9] Barnich O, Van Droogenbroeck M. ViBe:a universal background subtraction algorithm for video sequences. IEEE Transactions on Image Processing, 2011, 20(6):1709-1724 doi: 10.1109/TIP.2010.2101613
    [10] Wang L, Wang L, Wen M, Zhuo Q, Wang W Y. Background subtraction using incremental subspace learning. In: Proceedings of the 2007 IEEE International Conference on Image Processing. San Antonio, Texas, USA: IEEE, 2007. V-45-V-48
    [11] Seo J W, Kim S D. Recursive on-line (2D)2PCA and its application to long-term background subtraction. IEEE Transactions on Multimedia, 2014, 16(8):2333-2344 doi: 10.1109/TMM.2014.2353772
    [12] He J, Balzano L, Szlam A. Incremental gradient on the grassmannian for online foreground and background separation in subsampled video. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, Rhode Island, USA: IEEE, 2012. 1568-1575 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.662.1791&rep=rep1&type=pdf
    [13] Zhou X W, Yang C, Yu W C. Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(3):597-610 doi: 10.1109/TPAMI.2012.132
    [14] 沈燕飞, 李锦涛, 朱珍民, 张勇东, 代锋.基于非局部相似模型的压缩感知图像恢复算法.自动化学报, 2015, 41(2):261-272 http://www.aas.net.cn/CN/abstract/abstract18605.shtml

    Shen Yan-Fei, Li Jin-Tao, Zhu Zhen-Min, Zhang Yong-Dong, Dai Feng. Image reconstruction algorithm of compressed sensing based on nonlocal similarity model. Acta Automatica Sinica, 2015, 41(2):261-272 http://www.aas.net.cn/CN/abstract/abstract18605.shtml
    [15] Candés E J. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 2008, 346(9-10):589-592 doi: 10.1016/j.crma.2008.03.014
    [16] Baraniuk R. Compressive sensing. In: Proceedings of the 42nd Annual Conference on Information Sciences and Systems. Princeton, NJ, USA: IEEE, 2008. 4-5
    [17] Szabó Z, Lñrincz A. Distributed high dimensional information theoretical image registration via random projections. Digital Signal Processing, 2012, 22(6):894-902 doi: 10.1016/j.dsp.2012.04.018
    [18] Amador J. Random projection and orthonormality for lossy image compression. Image and Vision Computing, 2007, 25(5):754-766 doi: 10.1016/j.imavis.2006.05.018
    [19] Liu L, Fieguth P W. Texture classification from random features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3):574-586 doi: 10.1109/TPAMI.2011.145
    [20] Liu L, Fieguth P W, Clausi D, Kuang G Y. Sorted random projections for robust rotation-invariant texture classification. Pattern Recognition, 2012, 45(6):2405-2418 doi: 10.1016/j.patcog.2011.10.027
    [21] Liu L, Fieguth P W, Hu D W, Wei Y M, Kuang G Y. Fusing sorted random projections for robust texture and material classification. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(3):482-496 doi: 10.1109/TCSVT.2014.2359098
    [22] Johnson W, Lindenstrauss J. Extensions of lipschitz mappings into a Hilbert space. Contemporary Mathematics, 1984, 26:189-206 doi: 10.1090/conm/026
    [23] Diaconis P, Freedman D. Asymptotics of graphical projection pursuit. The Annals of Statistics, 1984, 12(3):793-815 http://cn.bing.com/academic/profile?id=5ece6b6fde4df69c965412100d9e9b9b&encoded=0&v=paper_preview&mkt=zh-cn
    [24] 朱碧婷, 郑世宝.基于高斯混合模型的空间域背景分离法及阴影消除法.中国图象图形学报, 2008, 13(10):1906-1909 doi: 10.11834/jig.20081022

    Zhu Bi-Ting, Zheng Shi-Bao. Space-domain background subtraction and shadow elimination based on Gaussian mixture model. Journal of Image and Graphics, 2008, 13(10):1906-1909 doi: 10.11834/jig.20081022
    [25] Gowreesunker B V, Tewfik A H. Learning sparse representation using iterative subspace identification. IEEE Transactions on Signal Processing, 2010, 58(6):3055-3065 doi: 10.1109/TSP.2010.2044251
    [26] 荆楠, 毕卫红, 胡正平, 王林.动态压缩感知综述.自动化学报, 2015, 41(1):22-37 http://www.aas.net.cn/CN/abstract/abstract18580.shtml

    Jing Nan, Bi Wei-Hong, Hu Zheng-Ping, Wang Lin. A survey on dynamic compressed sensing. Acta Automatica Sinica, 2015, 41(1):22-37 http://www.aas.net.cn/CN/abstract/abstract18580.shtml
    [27] Dai W, Milenkovic O. Subspace pursuit for compressive sensing signal reconstruction. IEEE Transactions on Information Theory, 2009, 55(5):2230-2249 doi: 10.1109/TIT.2009.2016006
    [28] He X C, Yung N H C. Corner detector based on global and local curvature properties. Optical Engineering, 2008, 47(5):Article No.057008
    [29] Zhao Y, Hong R C, Jiang J G. Visual summarization of image collections by fast RANSAC. Neurocomputing, 2016, 172:48-52 doi: 10.1016/j.neucom.2014.09.095
    [30] Wang Y, Jodoin P M, Porikli F, Konrad J, Benezeth Y, Ishwar P. CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Columbus, Ohio, USA: IEEE, 2014. 393-400 https://www.semanticscholar.org/paper/CDnet-2014%3A-An-Expanded-Change-Detection-Benchmark-Wang-Jodoin/45790b5bf6a3ad7c641809035661d14d73d6361b
    [31] Collins R, Zhou X, Teh S K. An open source tracking testbed and evaluation web site. In: Proceedings of the 2005 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS). Beijing, China: IEEE, 2005. 1-8
    [32] 秦明, 陆耀, 邸慧军, 吕峰.基于误差补偿的复杂场景下背景建模方法.自动化学报, 2016, 42(9):1356-1366 http://www.aas.net.cn/CN/abstract/abstract18924.shtml

    Qin Ming, Lu Yao, Di Hui-Jun, Lv Feng. An error compensation based background modeling method for complex scenarios. Acta Automatica Sinica, 2016, 42(9):1356-1366 http://www.aas.net.cn/CN/abstract/abstract18924.shtml
    [33] Sobral A. BGSLibrary: an openCV C++ background subtraction library. In: Proceedings of the 2013 IX Workshop de Visão Computacional. Rio de Janeiro, Brazil, 2013. 1-6
  • 加载中
图(12) / 表(4)
计量
  • 文章访问数:  2427
  • HTML全文浏览量:  310
  • PDF下载量:  819
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-01-23
  • 录用日期:  2017-05-06
  • 刊出日期:  2018-07-20

目录

    /

    返回文章
    返回