Improved Background Subtracted Method Based ViBe
CSTR:
Author:
  • Article
  • | |
  • Metrics
  • |
  • Reference [16]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    ViBe background subtracted algorithm deals with pixels based on RGB color space. So when the light suddenly changed, a large of background area will be misclassified as moving foreground. In addition, ViBe algorithm considered a lot of the moving background pixels to be foreground in the scene. To solve these problem, this paper proposed algorithm which combined with (r,g,I) normalized color space. The experimental results show that the improved algorithm can reaches the more robust extraction for foreground at the abrupt changes of light. At the same time, the improved algorithm has a better effect on detecting the moving background pixels in the scene.

    Reference
    1 Barnich O, Van Droogenbroeck M. ViBe: A universal background subtraction algorithm for video sequences. IEEE Trans. on Image Processing, 2011, 20(6): 1709-1724
    2 Van Droogenbroeck M, Paquot O. Background subtraction: experiments and improvements for ViBe. IEEE CVPR Workshop on Change Detection. 2012. 32-37.
    3 Brutzer S, Höferlin B, Heidemann G. Evaluation of background subtraction techniques for video surveillance. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). 2011. 1937-1944.
    4 樊晓亮,杨晋吉.基于帧间差分的背景提取与更新算法.计算机工程,2011,37(22).
    5 Wang H, Suter D. A consensus-based method for tracking: Modelling background scenario and foreground appearance. 2007. 1091-1105.
    6 Elgammal A, Harwood D, Davis LS. Non-parametric model for background subtraction. Sixth European Conference on Computer Vision. 2000. 751-767.
    7 McKenna SJ, et al. Tracking groups of people. Computer Vision Image Understanding, 2000, (80): 42-56.
    8 叶青,贺助理,湛强,雷辉.基于差分图像分块的视频背景提取算法.计算机工程和应用,2012,48(30).
    9 Hofmann M, Tiefenbacher P, Rigoll G. Background segmentation with feedback: The pixel-based adaptive segmenter. Proc IEEE CVPR Workshop on Change Detection. 2012. 38-43.
    10 邱祯艳,王修晖.一种结合Grabcut的Vibe目标检测算法.中国计量学院学报,2012,23(3).
    11 Kim K, Chalidabhongse T, Harwood D, Davis L. Real-time foreground-background segmentation using codebook model. Real-Time Imaging, June 2005, 11(3): 172-185.
    12 Manzanera A, Richefeu J. A new motion detection algorithm based on background estimation. Pattern Recognition Letters, February 2007, 28(3): 320-328.
    13 蒋建国,王涛,齐美彬,安红新.基于ViBe的车流量统计算法.电子测量与仪器学报,2012,26(6).
    14 Stauffer C, Grimson E. Adaptive background mixture models for real-time tracking. Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Ft. Collins, CO. Jun. 1999, 2: 246-252.
    15 Stauffer C, Grimson E. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell., Aug. 2000, 22(8): 747-757.
    16 高山,毕笃彦,魏娜.基于SACON背景模型的人体检测和跟踪.计算机应用,2009, 29(6).
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

严红亮,王福龙,刘志煌,沈士忠.基于ViBe算法的改进背景减去法.计算机系统应用,2014,23(6):130-134

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:November 01,2013
  • Revised:December 13,2013
  • Online: June 20,2014
Article QR Code
You are the first990606Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063