首页 > 论文 > 光学学报 > 36卷 > 6期(pp:615001--1)

基于改进视觉背景提取的运动目标检测算法

Moving Object Detection Algorithm Based on Improved Visual Background Extractor

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

针对视觉背景提取算法(ViBe)中出现的鬼影问题、不能很好适应背景高频扰动和摄像机抖动问题以及由于采用空间邻域扩散机制引起背景更新错误问题,提出一种改进的视觉背景提取算法。该算法结合视觉显著性判断背景模型中存在的鬼影目标,通过判断背景模型中每个像素点的鬼影程度,结合模糊准则自适应改变时间子采样因子,加快消除鬼影的速度;通过建立一个闪烁程度矩阵,判断背景高频扰动程度来设置自适应匹配阈值,加入小目标丢弃和空洞填充策略;统计前景像素24邻域区域的像素点个数,判断前景像素点是否为摄像机抖动或者背景更新错误引起的噪点,提高算法的稳健性。结果表明,改进后的算法可以很好地弥补经典ViBe算法的不足,准确率与识别率等指标均大大提升。

Abstract

Aiming at the problems of ghost, background turbulence in high frequency, camera jitter and error of background update caused by spatial propagation technique in classic visual background extractor(ViBe) algorithm, an improved ViBe algorithm is proposed. Combined with visual saliency, the new method determines whether the ghost target exists in the background model or not, and adaptively changes the time subsampling factor through the level of ghost for each pixel in the background model, which can improve the rate of ghost elimination. Self-adaptive threshold is adopted in the process of model matching by establishing a blinking degree matrix to judge the high-frequency disturbance level of background, so that the background model is better suitable for the dynamic background. Small object discard and hole filling strategies are added to the new method. It can judge if a foreground pixel is a noise point caused by camera jitter or an error of background update by counting pixel numbers in 24-connected neighboring region of foreground pixels. Therefore, it can improve the robustness of the algorithm. Experiments demonstrate that the improved algorithm is a good way to make up for the deficiency of the original ViBe algorithm. The accuracy and recognition rate are improved greatly.

投稿润色
补充资料

中图分类号:TP391.4

DOI:10.3788/aos201636.0615001

所属栏目:机器视觉

基金项目:国家自然科学基金(61401474)

收稿日期:2016-01-21

修改稿日期:2016-02-01

网络出版日期:--

作者单位    点击查看

莫邵文:国防科学技术大学电子科学与工程学院, 湖南 长沙 410073
邓新蒲:国防科学技术大学电子科学与工程学院, 湖南 长沙 410073
王帅:国防科学技术大学电子科学与工程学院, 湖南 长沙 410073
江丹:国防科学技术大学电子科学与工程学院, 湖南 长沙 410073
祝周鹏:中国人民解放军61541部队, 北京 100094

联系人作者:莫邵文(msw1992@163.com)

备注:莫邵文(1992-),男,硕士研究生,主要从事空间信息获取与处理、图像处理方面的研究。

【1】Benezeth Y, Jodoin P, Emile B, et al.. Review and evaluation of commonly-implemented background subtraction algorithms[C]. 19th International Conference on Pattern Recognition, 2008: 1-4.

【2】Ding Qi, Gu Guohua, Xu Fuyuan, et al.. Moving target detection on moving camera with the presence of strong parallax[J]. Laser & Optoelectronics Progress, 2015, 52(9): 091501.
丁祺, 顾国华, 徐富元, 等. 强视差下的移动相机运动目标检测[J]. 激光与光电子学进展, 2015, 52(9): 091501.

【3】Bernd Jahne. Digital image processing[M]. Berlin: Springer-Verlag, 2000: 7-13.

【4】Piccardi M. Background subtraction techniques: A review[C]. 2004 IEEE International Conference on Systems, Man and Cybernetics, 2004, 4: 3099-3104.

【5】Zhuang Zhemin, Zhang Congyou, Yang Jinyao, et al.. Investigation on visual background extractor based on gray feature and adaptive threshold[J]. Journal of Electronics & Information Technology, 2015, 37(2): 347-352.
庄哲民, 章聪友, 杨金耀, 等. 基于灰度特征和自适应阈值的虚拟背景提取研究[J]. 电子与信息学报, 2015, 37(2): 347-352.

【6】Chen Yin, Ren Kan, Gu Guohua, et al.. Moving object detection based on improved single Gaussian background model[J]. Chinese J Lasers, 2014, 41(11): 1109002.
陈银, 任侃, 顾国华, 等. 基于改进的单高斯背景模型运动目标检测算法[J]. 中国激光, 2014, 41(11): 1109002.

【7】Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction[M]. Berlin: Springer-Verlag, 2000: 751-767.

【8】Elgammal A, Duraiswami R, Harwood D, et al.. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance[J]. Proceedings of the IEEE, 2002, 90(7): 1151-1163.

【9】Tavakkoli A, Nicolescu M, Bebis G, et al.. Non-parametric statistical background modeling for efficient foreground region detection[J]. Machine Vision and Applications, 2008, 20(6): 395-409.

【10】Monari E, Pasqual C. Fusion of background estimation approaches for motion detection in non-static backgrounds[C]. 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, 2007: 347-352.

【11】Kim K, Chalidabhongse T, Harwood D, et al.. Background modeling and subtraction by codebook construction[C]. 2014 IEEE International Conference, 2004, 5: 3061-3064.

【12】Kim K, Chalidabhongse T, Harwood D, et al.. Real-time foreground-background segmentation using codebook model[J]. Special Issue on Video Object Processing, 2005, 11(3): 172-185.

【13】Barnich O, van Droogenbroeck M. ViBe: A universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.

【14】Barnich O, van Droogenbroeck M. ViBe: A powerful random technique to estimate the background in video sequences[C]. 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009: 945-948.

【15】Sanin A, Sanderson C, Lovell B. Shadow detection: A survey and comparative evaluation of recent methods[J]. Pattern Recognition, 2012, 45(4): 1684-1695.

【16】Prati A, Mikic I, Trive di M M, et al.. Detecting moving shadows: Algorithms and evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 918-923.

【17】Jodoin P M, Mignotte M, Konrad J. Statistical background subtraction using spatial cues[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2007, 17(12): 1758-1763.

【18】Shoushtarian B, Bez B. A practical adaptive approach for dynamic background subtraction using an invariant colour model and object tracking[J]. Pattern Recognition Letters, 2005, 26(1): 5-26.

【19】Borji A, Itti L. State-of-the-art in visual attention modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 185-207.

【20】Li Qingzhong, Zang Fengni, Zhang Yang. Ship target detection for moving video maritime surveillance[J]. Chinese J Lasers, 2014, 41(8): 0814001.
李庆忠, 臧风妮, 张洋. 动态视频监控中海上舰船目标检测[J]. 中国激光, 2014, 41(8): 0814001.

【21】Dick M, Ullman S, Sagi D. Parallel and serial process in motion detection[J]. Science, 1987, 237(4813): 400-402.

【22】Hou X D, Zhang L Q. Saliency detection: A spectral residual approach[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2007: 1-8.

【23】van Droogenbroeck M, Paquot O. Background subtraction: Experiments and improvements for ViBe[C]. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012: 32-37.

【24】Manzanera A, Richefeu J C. A new motion detection algorithm based on Σ-Δ background estimation[J]. Pattern Recognition Letters, 2007, 28(3): 320-328.

引用该论文

Mo Shaowen,Deng Xinpu,Wang Shuai,Jiang Dan,Zhu Zhoupeng. Moving Object Detection Algorithm Based on Improved Visual Background Extractor[J]. Acta Optica Sinica, 2016, 36(6): 0615001

莫邵文,邓新蒲,王帅,江丹,祝周鹏. 基于改进视觉背景提取的运动目标检测算法[J]. 光学学报, 2016, 36(6): 0615001

被引情况

【1】陈强,盛惠兴,张卓,谢迎娟,张学武. 红外光照突变下的运动目标检测. 激光与光电子学进展, 2016, 53(11): 111005--1

【2】夏振平,程成. 基于视觉显著性的立体显示图像深度调整. 光学学报, 2017, 37(1): 133001--1

【3】陈海永,郄丽忠,杨德东,刘 坤,李练兵. 基于超像素信息反馈的视觉背景提取算法. 光学学报, 2017, 37(7): 715001--1

【4】苏建东,齐晓慧,段修生. 基于单目视觉和棋盘靶标的平面姿态测量方法. 光学学报, 2017, 37(8): 815002--1

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF