期刊文献+

一种快速核密度估计背景建模方法 被引量:7

Fast kernel density estimation method for background modeling
下载PDF
导出
摘要 针对核密度估计背景建模方法运算量大难以实时应用的问题,提出了一种基于背景直方图分布的快速核密度估计背景建模方法。选用三角核函数进行核密度估计,根据三角核带宽函数的截断效应,引入背景分布的直方图完成快速背景建模,在保证目标检测准确性的同时提高运算速度。测试实验结果验证了算法能够满足监控系统的实时性要求。 For kernel density estimation is difficult to satisfy real-time applications because of large amount of calculation,this paper proposes a fast kernel density estimation method of background modeling based on the background histogram.Triangle kernel function is used to estimate the kernel density.According to the triangular truncation effect of kernel-bandwidth function,background samples histogram is built to complete the fast background modeling.The accuracy of target detection is ensured while processing speed is increased.Experimental results prove that the algorithm satisfies the real-time requirements of surveillance systems.
出处 《计算机工程与应用》 CSCD 2012年第5期192-193,203,共3页 Computer Engineering and Applications
关键词 背景建模 核密度估计 背景样本集直方图 实时性 background modeling kernel density estimation histogram of the background sample set real-timing
  • 相关文献

参考文献5

  • 1Colombari A, Fusiello A, Murino V.Segmentation and tracking of multiple video objects[J].Pattem Recognition, 2007,40(4 ) : 1307-1317.
  • 2Stauffer C, Grimson W E L.Adaptive background mixture models for real-time tracking[C]//Proceedings of the Computer Society on Computer Vision and Pattern Recognition,Fort Collins,USA, 1999:246-252.
  • 3Elgammal A M, Hanvood D, Davis L S.Non-parametric model for background subtraction[C]//Proceedings of the 6th European Conference on Computer Vision, Dublin, Ireland, 2000: 751-767.
  • 4毛燕芬,施鹏飞.一种核密度估计动态场景建模算法[J].数据采集与处理,2004,19(4):391-394. 被引量:5
  • 5胡闽,刘纯平,崔志明,王朝晖,张书奎.聚类差分图像核密度估计前景目标检测[J].中国图象图形学报,2009,14(10):2126-2131. 被引量:4

二级参考文献18

  • 1Lo B P L, Velastin S A. Automatic congestion detection system for underground platforms [ A ] . In: Proceedings of International Symposium on Intelligent Multimedia, Video, and Speech Processing [ C] , Hong Kong, China, 2001: 158-161.
  • 2Ridder C, Munkeh O, Kirchner H. Adaptive background estimation and foreground detection using Kalman-filtering [ A]. In: Proceedings of the Int' l Conference on Recent Advances Sinmechatronics [ C ], Istanbul, Turkey, 1995: 193-199.
  • 3Colombari A, Fusiello A, Murino V. Segmentation and tracking of multiple video objects [ J ] . Pattern Recognition, 2007, 40 (4) : 1307-1317.
  • 4Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking[ A]. In: Proceedings of the Computer Society on Computer Vision and Pattern Recognition [ C ] , FortCollins, USA, 1999:246-252.
  • 5Zivkovic Z. Improved adaptive Gaussian mixture model for backgroud subtraction [ A ]. In: Proceedings of the 17th International Conference on Pattern Recognition [ C ], Cambridge, United Kingdom, 2004: 28-31.
  • 6Elgammal A M, Hanvood D, Davis L S. Non-parametric model for background subtraction [ A ]. In: Proceedings of the 6th European Conference on Computer Vision [ C ], Dublin, Ireland, 2000: 751-767.
  • 7Mittal A, Paragios N. Motion-based background subtraction using adaptive kernel density estimation [ A ] . In: Proceedings of the Computer Society on Conference on Computer Vision and Pattern Recognition [ C ] , Washington D C, USA ,2004:302-309.
  • 8Li L, Huang W, Gu I Y H, et al. Foreground object detection from videos containing complex background [ A ]. In : Proceedings of 11 th ACM Multimedia Conference[ C ], Berkeley, USA, 2003:2-10.
  • 9Rosin P. Thresholding for change detection [ A ]. In: Proceedings of IEEE Int'l Conference on Computer Vision [ C ], Bombay, India, 1998:274-279.
  • 10Koller D, Weber J, Huang T, et al. Towards robust automatic traffic scene analysis in real-time [A].Proceedings of International Conference of Pattern Recognition[C]. Israel, 1994. 126~131.

共引文献7

同被引文献79

  • 1毛燕芬,施鹏飞.高斯核密度估计背景建模及噪声与阴影抑制[J].系统仿真学报,2005,17(5):1182-1184. 被引量:10
  • 2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. 被引量:97
  • 3齐美彬,魏星,蒋建国.基于三角核估计模型的运动目标检测方法[J].合肥工业大学学报(自然科学版),2006,29(4):389-391. 被引量:2
  • 4张磊,史金飞,罗翔.运动目标检测的差分图像法分析研究[J].工业仪表与自动化装置,2007(3):7-11. 被引量:11
  • 5Piccardi M. Background subtraction techmques:a re- view[C] // 2004 IEEE International Conference on Systems, Man and Cybernetics. Sydney: IEEE, 2004: 3099-3104.
  • 6Benezeth Y, Jodoin P M, Emile B, et al. Review and evaluation of commonlydmplemented background subtraction algorithms[C]//19th International Con- ference on Pattern Recognition. Tampa: IEEE, 2008 : 1-4.
  • 7SanMiguel J C, Maritinez J M. On the evaluation of background subtraction algorithms without ground- truth[C] // 2010 Seventh IEEE International Confer- ence on Advanced Video and Signal Based Surveil- lance. Boston: IEEE, 2010: 180-187.
  • 8Brutzer S, Hderlin B, Heidemann G. Evaluation ot background subtraction techniques for video surveii- lance[C] // 2011 IEEE Conference on Computer Vi- sion and Pattern Recognition. Providence: IEEE, 2011 : 1937-1944.
  • 9Lo B P L, Velastin S A. Automatic congestion detec- tion system for underground platform[C]//Proc 2001 ISIMP. Hong Kong; IEEE, 2001: 158-161.
  • 10Ismail H, Davide H, Larry S D. W4: real-time sur veillance of people and their activities [J]. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2000, 22(8): 809-830.

引证文献7

二级引证文献42

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部