期刊文献+

面向运动目标检测的ViBe算法改进 被引量:19

An Improved Vi Be Algorithm for Moving Object Detection
下载PDF
导出
摘要 背景差分法是静态背景下运动目标检测的常用方法,ViBe算法是它的主要建模方法之一.针对ViBe算法对鬼影消除缓慢的问题,提出了结合帧间差分技术的ViBe改进算法,使用帧间差分技术通过记录相关像素值的时域变化来判断鬼影像素,提高消除鬼影的速度.针对ViBe算法的固定阈值不能反映每个像素具体情况的问题,提出了一种自适应阈值的方法,可根据像素值的变化为每个像素设定阈值,提高前景检测的准确度.实验结果表明,结合帧间差分技术的ViBe算法能够较快地消除检测结果中的鬼影,应用自适应阈值的ViBe算法能够更准确地进行前景检测. Background differencing is the commonly used method for the detection of moving objects in the static background,and the ViBe algorithm is the main modeling approach.In order to solve the problem about low rate of ghost elimination caused by the execution of ViBe algorithm,an improved ViBe algorithm combining with frame difference method is proposed.By using the frame difference method,the ghost pixel is judged according to the changes in time domain for related pixel value,which can improve the rate of ghost elimination.Since the specific condition of each pixel cannot be reflected with the fixed threshold,a method with self-adaptive threshold is proposed.The threshold of each pixel is set according to the change of the pixel value,which can improve the accuracy of foreground detection.The experimental results show that the ViBe algorithm combining with frame difference technology can be used to eliminate the ghost in the detection results more quickly,and the foreground can be detected more accurately using the ViBe algorithm with self-adaptive threshold.
出处 《东北大学学报(自然科学版)》 EI CAS CSCD 北大核心 2015年第9期1227-1231,共5页 Journal of Northeastern University(Natural Science)
基金 国家科技支撑计划项目(2012BAH82F04)
关键词 运动目标检测 背景差分法 ViBe 算法 鬼影消除 自适应阈值 moving object detection background difference algorithm ViBe algorithm ghost elimination self-adaptive threshold
  • 相关文献

参考文献10

  • 1Barnich O, Droogenbroeck M V. ViBe: a universal background subtraction algorithm for video sequences [ J ]. IEEE Transactions on Image Processing, 2011, 20 ( 6 ) : 1709 - 1724.
  • 2何楠楠,杜军平.智能视频监控中高效运动目标检测方法研究[J].北京工商大学学报(自然科学版),2009,27(4):34-37. 被引量:14
  • 3Maddalena L, Petrosino A. A self-organizing approach to background subtraction for visual surveillance applications [J]. IEEE Transactions on Image Processing ,2008,17 (7) : 1168 - 1177.
  • 4Mclvor A M. Background subtraction techniques [J]. Process of linage and Vision Computing,2001,136(2) :752 -756.
  • 5Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction[M ]. Berlin: Springer Berlin Heidelberg ,2000:751 - 767.
  • 6Lucas D, Kanade T. An iterative image registration technique with an application to stereo vision[ C]// Proceedings of the 7th International Joint Conference on Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc, 1981: 674 - 679.
  • 7Bouwmans T, Porikli F0 Hoferlin B, et aI. Background modeling and foreground detection for video surveillance [ M]. Waretown :Chapman and HalI/CRC,2014.
  • 8Shoushtarian B, Bez H. A practical adaptive approach for dynamic background subtraction using an invariant color model and object tracking [J ]. Pattern Recognition, 2005, 26:5 -26.
  • 9Li Y Q,Chen W Z,Jiang R. The integration adjacent frame difference of improved ViBe for tbreground object detection [ C ]//The 7th International Conference on WiCOM. Piscataway : IEEE, 2011 : 1 - 4.
  • 10Droogenbroeck M V, Paquot O. Background subtraction: experiments and improvements for ViBe [ C ]//Computer Vision and Pattern Recognition Workshops (CVPRW). Providence : IEEE,2012 : 32 - 37.

共引文献13

同被引文献120

引证文献19

二级引证文献112

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