All Title Author
Keywords Abstract


复杂场景下自适应背景减除算法

DOI: 10.11834/jig.20150604

Keywords: 背景减除,混合高斯模型,onlineK-means,onlineEM,灰度值

Full-Text   Cite this paper   Add to My Lib

Abstract:

目的复杂场景下的背景减除是智能视频监控研究领域的研究重点和热点之一.针对混合高斯模型中高斯分布个数固定和参数初始化粗糙问题,提出一种应用于复杂场景中的基于混合高斯模型的自适应背景减除算法(AMGBS).方法通过灰度值归类算法自适应调整模型的高斯分布个数,使得背景模型能够适应场景的变化,并且结合在线K均值(onlineK-means)算法和在线期望最大化(onlineEM)算法初始化混合高斯模型参数.结果针对灰度值统计结果调整高斯分布数,以及采用优化参数初始化过程,实验表明,本文方法的平均查准率和平均查全率比传统的混合高斯算法高出10%左右,比其他改进的混合高斯算法高出2%左右.结论提出一种新的自适应背景减除算法,针对灰度值统计结果调整高斯分布数,以及采用优化参数初始化过程.实验结果表明,该方法对复杂场景有较强的适应能力,能够有效快速地完成背景减除,进而实现运动目标的提取.

References

[1]  Shen Y, Hu W, Liu J, et al. Efficient background subtraction for real-time tracking in embedded camera networks[C]//Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems. Toronto, ON, Canada: ACM, 2012: 295-308.
[2]  Bouwmans T. Recent advanced statistical background modeling for foreground detection-a systematic survey[J]. Recent Patents on Computer Science, 2011, 4(3): 147-176.
[3]  Liu X, Liu H, Qiang Z P, et al. Adaptive background modeling based on mixture gaussian model and frame subtraction[J]. Journal of Image and Graphics, 2008,13(4):729-734.[刘鑫, 刘辉, 强振平, 等. 混合高斯模型和帧间差分相融合的自适应背景模型[J]. 中国图象图形学报, 2008, 13(4): 729-734.][DOI:10.11834/jig.20080422]
[4]  Liu Z, Huang K, Tan T. Foreground object detection using top-down information based on em framework[J]. IEEE Transactions on Image Processing, 2012, 21(9): 4204-4217.
[5]  KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection[M]//Video-Based Surveillance Systems. Berlin: Springer, 2002: 135-144.
[6]  Apolinário L, Armesto N, Cunqueiro L. An analysis of the influence of background subtraction and quenching on jet observables in heavy-ion collisions[J]. Journal of High Energy Physics, 2013, 22: 1-33.
[7]  Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC: IEEE, 1999, 22(3):747-757.
[8]  Shah M, Deng J, Woodford B. Illumination invariant background model using mixture of gaussians and SURF features[C]//Computer Vision-ACCV 2012 Workshops. Berlin Heidelberg: Springer, 2013: 308-314.
[9]  Li Y, Li L. A novel split and merge EM algorithm for gaussian mixture model[C]// Proceedings of the 5th International Conference on Natural Computation. Tianjin: IEEE, 2009, 6: 479-483.
[10]  Singh A, Jaikumar P, Mitra S K, et al. Detection and tracking of objects in low contrast conditions[C]// Proceedings of IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics. Gandhinagar, India: IEEE, 2008: 98-103.
[11]  Alpaydin E. Introduction to Machine Learning[M]. Boston: MIT press, 2004:278-280.
[12]  Moon T K. The expectation-maximization algorithm[J]. Signal processing magazine, IEEE, 1996, 13(6): 47-60.
[13]  Nonaka Y, Shimada A, Nagahara H, et al. Evaluation report of integrated background modeling based on spatio-temporal features[C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, RI: IEEE, 2012: 9-14.
[14]  Zivkovic Z, van der Heijden F. Efficient adaptive density estimation per image pixel for the task of background subtraction[J]. Pattern recognition letters, 2006, 27(7): 773-780.
[15]  Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction[M]//Computer Vision―ECCV 2000. Dublin, Ireland:Springer, 2000: 751-767.
[16]  Hou Z Q, Han C Z. A background reconstruction algorithm based on pixel intensity classification[J]. Journal of Software, 2005,16(9):1568-1576.[侯志强, 韩崇昭. 基于像素灰度归类的背景重构算法[J]. 软件学报, 2005, 16(9): 1568-1576.]
[17]  Toyama K, Krumm J, Brumitt B, et al. Wallflower: principles and practice of background maintenance[C]// Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra: IEEE, 1999, 1: 255-261.
[18]  Goyette N, Jodoin P M, Porikli F, et al. Changedetection. net: a new change detection benchmark dataset[C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, RI: IEEE, 2012: 1-8.

Full-Text

comments powered by Disqus