基于改进ViBe算法的视频浓缩

doi:10.6040/j.issn.1672-3961.0.2016.306

摘要/Abstract

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

惠开发,成科扬,詹永照. 基于改进ViBe算法的视频浓缩[J]. 山东大学学报(工学版), 2017, 47(3): 43-48.

HUI Kaifa, CHENG Keyang, ZHAN Yongzhao. The video synopsis based on the enhanced ViBe algorithm[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 43-48.

参考文献

[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.

相关文章 15

[1]	索大翔,李波. 细粒度特征增强与尺寸匹配的光伏缺陷检测[J]. 山东大学学报 (工学版), 2025, 55(4): 9-17.
[2]	董明书,陈俐企,马川义,张珠皓,孙仁娟,管延华,庄培芝. 沥青路面内部裂缝雷达图像智能判识算法研究[J]. 山东大学学报 (工学版), 2025, 55(3): 72-79.
[3]	聂秀山,赵润虎,宁阳,刘新锋. 开放词汇目标检测方法综述[J]. 山东大学学报 (工学版), 2025, 55(1): 1-14.
[4]	张曼,孙凯军,李翔,孙纪舟. 融合FasterNet和RepVGG的安全设备佩戴检测方法[J]. 山东大学学报 (工学版), 2024, 54(6): 19-28.
[5]	陈晓燕,王川,齐明杰,张宁,林晓龙,霍延强,刘世杰,田源. 采用雷视融合方法的灌溉风险区异物入侵风险预警[J]. 山东大学学报 (工学版), 2024, 54(3): 115-121.
[6]	索大翔,李波. 基于Gromov-Wasserstein最优传输的输电线路小目标检测方法[J]. 山东大学学报 (工学版), 2024, 54(3): 22-29.
[7]	于畅,伍星,邓秋菊. 基于深度学习的多视角螺钉缺失智能检测算法[J]. 山东大学学报 (工学版), 2023, 53(4): 104-112.
[8]	余明骏,刁红军,凌兴宏. 基于轨迹掩膜的在线多目标跟踪方法[J]. 山东大学学报 (工学版), 2023, 53(2): 61-69.
[9]	刘丁菠,刘学艳,于东然,杨博,李伟. 面向小样本目标检测任务的自适应特征重构算法[J]. 山东大学学报 (工学版), 2022, 52(6): 115-122.
[10]	孟令灿,聂秀山,张雪. 基于遮挡目标去除的公交车拥挤度分类算法[J]. 山东大学学报 (工学版), 2022, 52(4): 83-88.
[11]	万鹏. 基于F-PointNet的3D点云数据目标检测[J]. 山东大学学报 (工学版), 2019, 49(5): 98-104.
[12]	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.
[13]	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.
[14]	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.
[15]	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.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed