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复杂背景下的自适应前景分割算法

DOI: 10.11834/jig.20110106

Keywords: 背景差,核密度,阈值估计

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Abstract:

复杂背景下的运动前景分割是计算机视觉领域研究的一个重点研究问题。为了对复杂背景下的运动前景进行有效分割,提出了一种复杂背景下自适应前景分割算法。该算法的背景模型是由一系列聚类和聚类的权重构成。每个聚类表示背景的一个历史状态,并能够根据背景的变化,自适应创建、更新或删除聚类,使得背景模型能够准确反映出场景的变化。每个聚类权重是根据聚类的大小和更新时间自动确定的。为了自动确定该方法的重要阈值,还提出一种基于非参数密度估计的阈值估计方法,并在不同的场景下与多个背景建模方法进行了比较,实验结果表明,该算法是有效的。

References

[1]  Sheikh Yaser;Shah Mubexak,Bayesian modeling of dynamic scenes for object detection,IEEE Transactions on Pattern Analysis and Machine Intelligence,2005(11).
[2]  Heikkila M;Pietikainen M,A texture-based method for modeling the background and detecting moving objects,IEEE Transactions OH Pattern Analysis and Machine Intelligence,2006(04).
[3]  Zhong Jing;Sclaroff Stan,Segmenting foreground objects from a dynamic textured background via a robust Kalman filter,Washington,DC,USA:IEEE Computer Society,2003.
[4]  Stauffer C;Grimson E L,Learning patterns of activity using realtime tracking,IEEE Transactions on Pattern Analysis and Machine Intelligence,2000(03).
[5]  Ahmed Elgammal ;Ramani Duraiswami ;David Harwood ;Larry D. Davis,Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,Proceedings of the IEEE?,2002, 90(7).
[6]  Lee D S,Effective Gaussian mixture learning for video background subtraction,IEEE Transactions on Pattern Analysis and Machine Intelligence,2005(05).
[7]  Zoran Zivkovic ;Ferdinand van der Heijden,Efficient adaptive density estimation per image pixel for the task of background subtraction,Pattern Recognition Letters?,2006, 27(7).
[8]  Dar-Shyang Lee,Effective Gaussian mixture learning for video background subtraction,IEEE Transactions on Pattern Analysis and Machine Intelligence?,2005, 27(5).
[9]  Ahmed Elgammal;Ramani Duraiswami;David Harwood,Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,Proceedings of the IEEE,2002(07).
[10]  Stauffer C;Grimson E L,Learning patterns of activity using realtime tracking,IEEE Transactions on Pattern Analysis and Machine Intelligence,2000(03).
[11]  Zhong Jing;Sclaroff Stan,Segmenting foreground objects from a dynamic textured background via a robust Kalman filter,Washington,DC,USA:IEEE Computer Society,2003.
[12]  Heikkila, M. ;Pietikainen, M.,A texture-based method for modeling the background and detecting moving objects,IEEE Transactions on Pattern Analysis and Machine Intelligence?,2006, 28(4).
[13]  Sheikh, Y. ;Shah, M.,Bayesian modeling of dynamic scenes for object detection,IEEE Transactions on Pattern Analysis and Machine Intelligence?,2005, 27(11).
[14]  Maddalena, L. ;Petrosino, A.,A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications,IEEE Transactions on Image Processing?,2008, 17(7).
[15]  Tsai Duming,A fast thresholding selection procedure for multimodal and unimodal histograms,Pattern Recognition Letters,1995(06).
[16]  Sheather S J;Jones M C,A reliable data-based bandwidth selection method for kernel density estimation,Journal of the Royal Statistical Society,Series B:Statistical Methodology,1991(03).
[17]  Zivkovic Z;van der Heijden F,Efficient adaptive density estimation per image pixel for the task of background subtraction,Pattern Recognition Letters,2006(07).
[18]  Sheather S J;Jones M C,A reliable data-based bandwidth selection method for kernel density estimation,Journal of the Royal Statistical Society,Series B:Statistical Methodology,1991(03).
[19]  Tsai Duming,A fast thresholding selection procedure for multimodal and unimodal histograms,Pattern Recognition Letters,1995(06).
[20]  Maddalena L;Petrosino A,A self-Organizing approach to background subtraction for visual surveillance applications,IEEE Transactions on Image Processing,2008(07).

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