基于改进的混合高斯模型的运动目标检测方法

王东方; 王玉德; 王景武

doi:10.7510/jgjs.issn.1001-3806.2014.06.011

基于改进的混合高斯模型的运动目标检测方法

1.
曲阜师范大学物理工程学院, 曲阜 273165

作者简介: 王东方(1985-),男,硕士研究生,主要研究方向为多媒体信息处理、模式识别。.

通讯作者: 王玉德, wyude-01@163.com ;

中图分类号: TP391

Moving target detection algorithm based on improved Gaussian mixture model

1.
College of Physics and Engineering, Qufu Normal University, Qufu 273165, China

Corresponding author: WANG Yude, wyude-01@163.com ;

CLC number: TP391

摘要: 为了改善混合高斯模型在光照突变时容易产生大量误检的缺陷,采用了一种高斯模型与均值法相结合并为前景像素建立计数器的方法。在建立背景模型时,运用多帧图像求平均值的方法初始化混合高斯模型的背景;为每帧图像的前景像素数建立计数器,并以此消除被误判为前景的区域;对检测出的前景区运用数学形态学处理,得到图像真正的前景区域。结果表明,该算法不仅克服了初始背景中的干扰,而且消除了光照突变时的误检,提高了运动目标的检测率。
- 图像处理 /
- 运动目标检测 /
- 混合高斯模型 /
- 光照影响
Abstract: In order to eliminate the defects of false detection of mixed Gaussian model under sudden illumination, a new algorithm combining Gaussian model with average background method was proposed to count the foreground pixels. Firstly, the background of Gaussian mixture model was initialized by using multi-frame averaging method when building the background model. Secondly, a counter for the number of foreground pixels of every frame was established and the false detection was eliminated based on the counter. Finally, the target was detected by using mathematical morphology and the foreground of the image was gotten. The results show that this improved algorithm not only overcomes the interference of the initial background but also eliminates the false detection when the illumination changes, and improves the detection rate of the moving targets.
- image processing /
- moving target detection /
- Gaussian mixture model /
- illumination effect

[1]	BAI Y Ch, ZHANG X G, TANG L. Transverse velocity estimation based on Wigner-Hough transform[J].Journal of Nanjing University(Natural Science Edition), 2010, 46(4): 366-369(in Chi-nese).
[2]	HARITAOGLU I, HARWOOD D, DAVIS L S.Real-time surveil- lance of people and their activities[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 809-830.
[3]	WEI X F, LIU X. Research of image segmentation based on 2-D maximum entropy optimal threshold[J].Laser Technology, 2013, 37(4): 519-522(in Chinese).
[4]	HAO H G, CHEN J Q. Moving object detection algorithm based on five frame difference and background difference[J]. Computer Engineering, 2012, 38(4): 146-148(in Chinese).
[5]	TSAI D M, LAI S C. Independent component analysis based background subtraction for indoor surveillance[J].IEEE Transactions on Image Processing, 2009, 18(1): 158-160.
[6]	GUPT S, MASOUND O, MARTIN R F K, et al. Detection and classification for vehicles[J].IEEE Transactions on Intelligent Transportation Systems, 2002, 1(3): 37-47.
[7]	ZHOU L, ZHU H. Optical flow calculation based on dual subtraction for motion detection[J].Computer Simulation, 2009, 26(12): 168-171(in Chinese).
[8]	HE G M, LI L J, JIA Zh T. A rapid video segmentation algorithm based on symmetrical DFD[J].Mini-micro Systems, 2003, 24(6): 966-968(in Chinese).
[9]	BENNETT B, MAGEE D R, COHN A G, et al. Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity[J].Image and Vision Computing, 2008, 26(1): 67-81.
[10]	GAN Sh X. Moving targets detection using codebook[J].Journal of Image and Graphics, 2008, 13(2): 365-370(in Chinese).
[11]	MEI N N, WANG Zh J. Moving object detection algorithm based on Gaussian mixture model[J]. Computer Engineering and Design, 2012, 33(8): 3149-3153(in Chinese).
[12]	LI Y N, YU X C, TANG F, et al. Application of improved optic flow field in the supervisory control of nuclear explosion[J]. Laser Technology, 2013, 37(1): 118-120(in Chinese).
[13]	ZHOU J Y, WU X P, ZHANG Ch, et al. A moving object detection method based on sliding window gaussian mixture model[J].Journal of Electronics and Information Technology, 2013, 35(7): 1650-1656(in Chinese).
[14]	XU K, CHEN Sh X, YAN G. Moving object detection based on improved Gaussian model [J].Laser and Infrared, 2012, 42(7): 821-824(in Chinese).
[15]	NADIMI S, BEHAN B. Physical models for moving shadow and object detection in video[J].IEEE Transactions on Pattern Ana-lysis and Machine Intelligence, 2004, 26(8): 1079-1087.
[16]	TU L F, PENG Q, ZHONG S D. A moving object detection method adapted to camera jittering[J].Journal of Electronics Information Technology,2013,35(8):1914-1920(in Chinese).

[1]	孙越娇 , 雷武虎 , 胡以华 , 赵楠翔 , 任晓东 . 基于视觉显著模型的遥感图像舰船快速检测. 激光技术, 2018, 42(3): 379-384. doi: 10.7510/jgjs.issn.1001-3806.2018.03.017
[2]	李文龙 , 戈海龙 , 任远 , 成巍 . 图像处理技术在激光熔池温度检测的应用. 激光技术, 2018, 42(5): 599-604. doi: 10.7510/jgjs.issn.1001-3806.2018.05.004
[3]	刘逸飞 , 苏亚 , 姚晓天 , 崔省伟 , 杨丽君 , 周聪聪 , 何松 . OCT无创血糖检测图像处理最优化方法研究. 激光技术, 2023, 47(2): 178-184. doi: 10.7510/jgjs.issn.1001-3806.2023.02.004
[4]	江天 , 沈会良 , 杨冬晓 , 刘建军 , 邹哲 . 基于模糊局部信息C均值的太赫兹图像目标检测. 激光技术, 2015, 39(3): 289-294. doi: 10.7510/jgjs.issn.1001-3806.2015.03.001
[5]	王其华 , 叶苗 . 基于裂变自举粒子滤波的红外目标跟踪处理. 激光技术, 2011, 35(1): 141-144. doi: 10.3969/j.issn.1001-3806.2011.01.038
[6]	李昌海 , 叶玉堂 , 沈淦松 , 徐伟 , 叶涵 , 姚景昭 . 基于图像轮廓分析的LCD线路缺陷检测. 激光技术, 2013, 37(2): 207-210. doi: 10.7510/jgjs.issn.1001-3806.2013.02.017
[7]	吴超 , 王寿松 . 基于轨迹图像的雾滴颗粒运动速度光电测量方法. 激光技术, 2014, 38(2): 214-217. doi: 10.7510/jgjs.issn.1001-3806.2014.02.015
[8]	王春娴 , 李会山 , 王风忠 . 基于图像的激光熔覆粉末粒子运动行为分析. 激光技术, 2010, 34(3): 300-302,319. doi: 10.3969/j.issn.1001-3806.2010.03.004
[9]	谢军昱 , 许杨剑 , 王效贵 . 基于贝叶斯模型和数字图像相关的视觉测量. 激光技术, 2016, 40(6): 866-870. doi: 10.7510/jgjs.issn.1001-3806.2016.06.019
[10]	纪文 , 孙水发 , 王帅 , 董方敏 . 对数域的光学相干层析图像噪声模型分析. 激光技术, 2014, 38(6): 848-853. doi: 10.7510/jgjs.issn.1001-3806.2014.06.027
[11]	何易德 , 朱斌 , 姜湖海 , 刘书信 , 李黎明 , 胡绍云 . 红外图像多尺度统计和应用先验去模糊模型. 激光技术, 2023, 47(3): 360-365. doi: 10.7510/jgjs.issn.1001-3806.2023.03.012
[12]	陈树越 , 朱双双 , 蒋星 , 徐扬 . 基于高斯型点扩展函数的红外图像热源复原. 激光技术, 2016, 40(2): 270-273. doi: 10.7510/jgjs.issn.1001-3806.2016.02.025
[13]	李泽峰 , 欧阳八生 . 基于MFC+HALCON图像识别Mark圆的检测方法. 激光技术, 2020, 44(3): 358-363. doi: 10.7510/jgjs.issn.1001-3806.2020.03.016
[14]	刘煊 , 渠慎明 . 低秩稀疏和改进SAM的高光谱图像误标签检测. 激光技术, 2022, 46(6): 808-816. doi: 10.7510/jgjs.issn.1001-3806.2022.06.016
[15]	何易德 , 朱斌 , 王询 , 陈好 , 贾静 . 基于跟踪特征的目标模型逼真度评估方法. 激光技术, 2020, 44(4): 515-519. doi: 10.7510/jgjs.issn.1001-3806.2020.04.020
[16]	张海庄 , 姚梅 , 雷萍 , 李鹏 , 曾庆平 . 远场激光光斑图像处理方法研究. 激光技术, 2013, 37(4): 460-463. doi: 10.7510/jgjs.issn.1001-3806.2013.04.010
[17]	张宝华 , 刘鹤 . 基于区域定位与轮廓分割的红外目标检测. 激光技术, 2015, 39(6): 840-844. doi: 10.7510/jgjs.issn.1001-3806.2015.06.023
[18]	汤敏 , 王惠南 . 激光扫描共聚焦显微镜图像的计算机处理. 激光技术, 2007, 31(5): 558-560.
[19]	张羽鹏 , 王开福 . LabVIEW和MATLAB在电子散斑干涉图像处理中的应用. 激光技术, 2009, 33(6): 582-585,589. doi: 10.3969/j.issn.1001-3806.2009.06.007
[20]	冯煦 , 张瑞瑛 , 周萍 , 李松 . 大功率半导体线激光图像处理方法研究. 激光技术, 2010, 34(5): 624-627. doi: 10.3969/j.issn.1001-3806.2010.O5.013

点击查看大图

计量

文章访问数: 2969
HTML全文浏览量: 672
PDF下载量: 496
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

基于改进的混合高斯模型的运动目标检测方法

作者简介: 王东方(1985-),男,硕士研究生,主要研究方向为多媒体信息处理、模式识别。.

通讯作者: 王玉德, wyude-01@163.com ;

Moving target detection algorithm based on improved Gaussian mixture model

Corresponding author: WANG Yude, wyude-01@163.com ;

计量

基于改进的混合高斯模型的运动目标检测方法

通讯作者: 王玉德, wyude-01@163.com;

作者简介: 王东方(1985-),男,硕士研究生,主要研究方向为多媒体信息处理、模式识别。

English Abstract

Moving target detection algorithm based on improved Gaussian mixture model

Corresponding author: WANG Yude, wyude-01@163.com

全文HTML

目录

留言板

基于改进的混合高斯模型的运动目标检测方法

作者简介: 王东方(1985-),男,硕士研究生,主要研究方向为多媒体信息处理、模式识别。.

通讯作者: 王玉德, wyude-01@163.com ;

Moving target detection algorithm based on improved Gaussian mixture model

Corresponding author: WANG Yude, wyude-01@163.com ;

计量

出版历程

基于改进的混合高斯模型的运动目标检测方法

通讯作者: 王玉德, wyude-01@163.com;

作者简介: 王东方(1985-),男,硕士研究生,主要研究方向为多媒体信息处理、模式识别。

English Abstract

Moving target detection algorithm based on improved Gaussian mixture model

Corresponding author: WANG Yude, wyude-01@163.com

全文HTML

目录