您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（工学版）》

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (3): 43-48.doi: 10.6040/j.issn.1672-3961.0.2016.306

• • 上一篇    下一篇

基于改进ViBe算法的视频浓缩

惠开发,成科扬,詹永照   

  1. 江苏大学计算机科学与通信工程学院, 江苏 镇江 212013
  • 收稿日期:2016-07-22 出版日期:2017-06-20 发布日期:2016-07-22
  • 作者简介:惠开发(1992— ),男,江苏盐城人,硕士研究生,主要研究方向为计算机视觉、视频语义. E-mail: adamhui@126.com
  • 基金资助:
    江苏省重点研发计划资助项目(BE2015137);镇江市科技计划资助项目(SH2014017);江苏大学高级人才科研启动基金资助项目(15JDG180)

The video synopsis based on the enhanced ViBe algorithm

HUI Kaifa, CHENG Keyang, ZHAN Yongzhao   

  1. School of Computer Science and Telecommunications Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
  • Received:2016-07-22 Online:2017-06-20 Published:2016-07-22

PDF (PC)

410

摘要： 针对监控视频在时间上存在冗余的问题,对ViBe(visual background extractor)算法进行改进,解决了ViBe算法存在噪声和易引入鬼影的问题,通过改进后的算法对视频进行背景建模,并对得到的背景掩模提取外轮廓以确定视频帧中是否存在前景对象。将存在前景对象的视频帧写入视频流中,达到视频浓缩的目的。经过试验验证,该方法可以有效地减少视频中的冗余信息,减小视频的体积,视频中的重要信息同时也得到了完整保留,满足实时性要求。

关键词: ViBe算法, 视频浓缩, 目标检测, 鬼影抑制, 背景建模, 视频监控

Abstract: Focusing on the time redundancy of surveillance video, an enhanced ViBe was proposed to solve the problems of noise and the ghost in ViBe algorithm. The improved algorithm was applied in the procession of video background modelling. It could be determined whether there was a foreground object in a certain frame by extracting outside contour of the obtained binary image, and the frames contains foreground objects would be pushed into the video stream for the purpose of video synopsis. After the experimental verification, it could be concluded that the method could effectively reduce the redundant information in the video and the volume of the video. Meanwhile some important information in the video could be retained, and the algorithm satisfied the requirement of real-time.

Key words: video surveillance, video synopsis, background modelling, object detection, ViBe algorithm, ghost suppression

中图分类号: 

  • TP391
[1] 王娟, 蒋兴浩, 孙锬锋.视频摘要技术综述[J]. 中国图象图形学报, 2014, 19(12):1685-1695. WANG Juan, JIANG Xinghao, SUN Tanfeng. Review of video abstraction[J].Journal of Image and Graphics, 2014, 19(12):1685-1695.
[2] ZHU X, CHEN L C, GONG S. Video synopsis by heterogeneous multi-source Correlation[C] //IEEE International Conference on Computer Vision. Sydney, Australia: IEEE, 2013:81-88.
[3] RAVACHA A, PRITCH Y, PELEG S. Making a long video short: dynamic video synopsis[C] //IEEE Computer Society Conference on Computer Vision & Pattern Recognition. New York, USA: IEEE Computer Society, 2006:435-441.
[4] LI K, YAN B, WANG W, et al. An effective video synopsis approach with seam carving[J]. Signal Processing Letters IEEE, 2016, 23(1):11-14.
[5] HUANG C R, CHEN H C, CHUNG P C. Online surveillance video synopsis[C] //IEEE International Symposium on Circuits and Systems. Seoul, Korea: IEEE, 2012.
[6] 韩建康. 基于运动检测及跟踪的视频浓缩方法研究[D]. 北京:北京邮电大学, 2012. HAN Jiankang. Moving area detection and tracking based video condensation[D]. Beijing: Beijing University of Posts and Telecommunications, 2012.
[7] NAM J H, TEWFIK A H. Video abstract of video[C] //Multimedia Signal Processing. Copenhagen, Denmark: IEEE, 1999:117-122.
[8] PETROVIC N, JOJIC N, HUANG T S. Adaptive video fast forward[J]. Multimedia Tools & Applications, 2005, 26(3):327-344.
[9] PENG J, QIN X. Keyframe-based video summary using visual attention clues[J]. IEEE Multimedia, 2009, 17(2):64-73.
[10] 王秀芬, 王汇源, 王松. 基于背景差分法和显著性图的海底目标检测方法[J]. 山东大学学报(工学版), 2011, 41(1):12-16. WANG Xiufen, WANG Huiyuan, WANG Song. Underwater object detection based on background subtraction and a saliency map[J]. Journal of Shandong University(Engineering Science), 2011, 41(1):12-16.
[11] 王海军, 葛红娟, 张圣燕. 基于L1范数和最小软阈值均方的目标跟踪算法[J]. 山东大学学报(工学版), 2016, 46(3):14-22. WANG Haijun,GE Hongjuan, ZHANG Shengyan. Object tracking via L1 norm and least soft-threshold square[J]. Journal of Shandong University(Engineering Science), 2016, 46(3):14-22.
[12] HUANG C R, CHUNG P C J, YANG D K, et al. Maximum a posteriori probability estimation for online surveillance video synopsis[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2014, 24(8):1417-1429.
[13] BARNICH O, VAN D M. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2011, 20(6):1709-1724.
[14] BARNICH O, DROOGENBROECK M V. ViBe: a powerful random technique to estimate the background in video sequences[C] //IEEE International Conference on Acoustics. Taipei, China: IEEE, 2009:945-948.
[15] 张磊, 傅志中, 周岳平. 基于HSV颜色空间和ViBe算法的运动目标检测[J]. 计算机工程与应用, 2014, 50(4):181-185. ZHANG Lei, FU Zhizhong, ZHOU Yueping. Moving objects detection based on HSV colorspace and Vibe algorithm[J]. Computer Engineering and Applications, 2014, 50(4):181-185.
[16] 王文豪, 周泓, 严云洋. 一种基于连通区域的轮廓提取方法[J]. 计算机工程与科学, 2011, 33(6):67-71. WANG Wenhao, ZHOU Hong, YAN Yunyang. An approach to contour extraction based on connected regions[J]. Computer Engineering and Science, 2011, 33(6):67-71.
[17] 孙水发, 覃音诗, 马先兵,等. 室外视频前景检测中的形态学改进ViBe算法[J]. 计算机工程与应用, 2013, 49(10):159-162. SUN Shuifa, QIN Yinshi, MA Xianbing, et al. ViBe foreground detection algorithm and its improvement with morphology post-processing for outdoor scene[J]. Computer Engineering and Applications, 2013, 49(10):159-162.
[18] LI L, HUANG W, GU Y H, et al. Statistical modeling of complex backgrounds for foreground object detection[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Proceeding Society, 2004, 13(11):1459-1472.
[19] FERRYMAN J, SHAHROKNI A. PETS 2009: dataset and challenge[C] //The 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. Miami, USA: IEEE, 2009:1-6.
[20] WANG W H, ZHOU H, YAN Y Y. An approach to contour extraction based on connected regions[J]. Computer Engineering and Science, 2011, 33(6):67-71.
[21] SUZUKI S, BE K. Topological structural analysis of digitized binary images by border following[J]. Computer Vision Graphics & Image Processing, 1985, 30(1):32-46.
[1] 刘英霞,王希常,唐晓丽,常发亮. 基于小波域特征和贝叶斯估计的目标检测算法[J]. 山东大学学报(工学版), 2017, 47(2): 63-70.
[2] 邱晓欣1,2,张文强1,2*,秦晋贤1,2,杜正阳1,2,张德峰1,2. 恶劣环境下多目标实时跟踪算法研究[J]. 山东大学学报(工学版), 2014, 44(2): 21-27.
[3] 杨健梅1,黄添强1,2*,江伟坚1. 基于人脸色温的拼接图像篡改检测[J]. 山东大学学报(工学版), 2013, 43(5): 24-30.
[4] 王秀芬,王汇源,王松. 基于背景差分法和显著性图的海底目标检测方法[J]. 山东大学学报(工学版), 2011, 41(1): 12-16.
[5] 乔伟1,王汇源1,2,吴晓娟1,刘鹏威1. 基于混沌动力学模型的群体目标检测与分类[J]. 山东大学学报(工学版), 2010, 40(2): 19-23.
[6] 吕行,史忠科 . DirectShow框架下实时运动目标检测与跟踪方法的研究与应用[J]. 山东大学学报(工学版), 2007, 37(6): 5-9 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(工学版)》编辑部　
地址: 济南市山大南路27号（250100）　 电话: 0531-88366735　 E-mail: xbgxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发