期刊文献+

基于可信度评估的背景建模方法

Background modeling based on credibility evaluation
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摘要 传统算法在背景建模过程中孤立地对像素进行分析,在处理缓慢移动物体和往复运动物体时常常发生误判。针对这一情况,提出一种新的背景建模方法。该方法通过对像素值稳定性、像素值出现频率以及像素间的空间联系统计分析,评估出每一像素值作为背景的可信程度,进而得出背景。对比已有的背景建模算法,该方法在分析过程中考虑了物体的整体属性,在适应环境的变化与背景扰动的同时,能明显减少现有算法对缓慢移动物体和往复运动物体的误判。 Most existing algorithms analyze pixels separately,which leads to misjudegment in processing slow-moving objects and reciprocating-moving objects.Concerning this problem,one new method was presented.This method evaluated the credibility of every pixel value as background by analyzing the stability,showing frequency and spatial relations of one pixel value.Compared to the existing algorithms,this algorithm not only adapts to environmental changes and background disruption but also adapts to slow-moving object and reciprocating-moving objects.
出处 《计算机应用》 CSCD 北大核心 2012年第6期1532-1535,1556,共5页 journal of Computer Applications
关键词 背景建模 视频监控 可信度评估 空间联系 图像分割 background modeling video monitoring credibility evaluation spatial relation image segmentation
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参考文献13

  • 1WREN C R, AZARBAYEJANI A, DARRELL T, et al. Pfinder: Reahime tracking of the human body [ J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780- 785.
  • 2SUO PENG, WANG YANJIANG. An improved adaptive background modeling algorithm based on Gaussian mixture model[ C]//9th In- ternational Conference on Signal Processing. Piscataway: IEEE Press, 2008:1436 - 1439.
  • 3TURDU D, ERDOGAN H. Improving Gaussian mixture model based adaptive background modeling using hysteresis thresholding [ C] // 15th Signal Processing and Communications Applications. Piscataway: IEEE Press, 2007:997 - 1000.
  • 4HARVILLE M. A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models[ C]//7th Euro- pean Conference on Computer Vision. London: Springer-Verlag, 2002:543-560.
  • 5ZIVKOVIC Z. Improved adaptive Gaussian mixture model for back- ground subtraction[ C]// 17th International Conference on Pattern Recognition. Washington, DC: IEEE Computer Society, 2004:28 -31.
  • 6KIM K, CHALIDABHONGSE T H, HARWOOD D, et al. Real-time foreground-background segmentation using codebook model [ J]. Re- al-Time Imaging, 2005, 11 (3) : 172 - 185.
  • 7ILYAS A, SCUTURICI M, MIGUET S. Real time foreground-back- ground segmentation using a modified codebook model[ C]// 6th IEEE International Conference on Advanced Video and Signal Based Surveillance. Washington, DC: IEEE Computer Society, 2009:454 -459.
  • 8ELGAMMAL A, DURAISWAMI R, HARWOOD D, et al . Background and foreground modeling using nouparanletric Kernel density estimation for visual surveillance [ J ]. Proceedings of the IEEE, 2002, 90(7) :1151 - 1163.
  • 9LIU YAZHOU, YAO HONGXUN. Nonparametric background generation [ J ]. Journal of Visual Communication and Image Representation, 2007, 18(3 ) :253 -263.
  • 10RIDDER C, MUNKELT O, KIRCHNER H. Adaptive background estimation mid foreground detection using Kalman-fihering[ C ]// Proceedings of the International Conference on Recent Advances in Mechatronics. lstanbul: UNESCO Chair on Mechatronics, 1995: 193 - 199.

二级参考文献10

  • 1[1]Barron J, Fleet D, Beauchemin S. Performance of optical flow techniques [J]. International Journal of Computer Vision, 1994, 12(1):42~77.
  • 2[2]Lipton A Fujiyoshi H, Patil R. Moving target classification and tracking from real-time video [J]. Proc. of WACV'98, 1998, 8~14.
  • 3[3]Robert T. Collins, et al.. A system for video Surveillance and Monitoring [D] Technical Report CMU-RI-TR-00-12, Carnegie Mellon University 2000, http://www.cs.cmu.edu.
  • 4[4]Stauffer C, Grimson W. Adaptive background mixture models for real-time tracking [J]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999, 2(6):248~252.
  • 5[5]Jiang C, Matthew. Shadow segmentation and classification in a constrained environment [J]. CVGIP, 1994, 55(2):213~225.
  • 6[6]Cucchiara R, Grana C, Piccardi M, Prati A. Statistical and knowledge-based moving object detection in traffic scene [J]. Proceedings of IEEE Int'l Conference on Intelligent Transportation Systems, Oct. 2000, 27~32.
  • 7[7]Trivedi MM, Mikic I, Kogut G. Distributed video networks for incident detection and management [J]. Proceedings of IEEE Int'l Conference on Intelligent Transportation Systems, Oct. 2000, 155~160.
  • 8[8]韩光文.系统辩识[M].武汉:华中理工大学出版社,1988.
  • 9[9]魏平.光学Hadamrd编码技术的研究[M].北京理工大学,1984.
  • 10常红,王涌天,阎达远,周雅,华宏,徐彤.针对虚拟现实跟踪技术的快速滤波算法[J].光学学报,2000,20(9):1224-1228. 被引量:2

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