Moving target detection of the video images

Tong-xue ZHOU; Ming ZHU

doi:10.3788/YJYXS20173201.0040

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Moving target detection of the video images

Display and Image | 更新时间：2020-07-07

- Moving target detection of the video images
- Chinese Journal of Liquid Crystals and Displays Vol. 32, Issue 1, Pages: 40-47(2017)
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20173201.0040
  CLC： TN919.8;TP391.4
- Received：24 August 2016，
  
  Accepted：07 September 2016，
  
  Published：2017-01
- 稿件说明：
移动端阅览
Tong-xue ZHOU, Ming ZHU. Moving target detection of the video images[J]. Chinese journal of liquid crystals and displays, 2017, 32(1): 40-47.
DOI：

Tong-xue ZHOU, Ming ZHU. Moving target detection of the video images[J]. Chinese journal of liquid crystals and displays, 2017, 32(1): 40-47. DOI： 10.3788/YJYXS20173201.0040.

摘要

运动目标检测，是指从视频图像中将运动变化区域提取出来的检测技术，是图像处理技术的基础。在军事公安、交通管理、视频监控、医学检查等领域应用广泛。为了改进单独采用帧差法或背景减法进行运动目标检测时存在的不足，本文提出一种利用边缘信息的三帧差法与基于混合高斯模型的背景减法相结合的运动目标检测算法。该方法对视频图像中连续的三帧图像两两差分，对3个差分图像取均值，二值化，再经过形态学处理，并对中间帧进行Canny边缘提取，将二者进行“与”运算，即得到运动目标的边缘，用背景减法提取中间帧的前景，二值化，将其和目标的边缘进行“或”运算，经过形态学处理便可得到运动目标。实验结果表明，使用该方法目标检出率提高了9.7%~72.1%，误检率降低了0.090%~2.900%。这种二者相结合的方法相对于单一的检测算法能够有效、可靠地提取出运动目标。

Abstract

Moving target detection technology refers to extracting the change from the video images

and it is widely used in the field of military security

traffic management

video surveillance

medical examination and so on. In order to improve the shortcomings of three-frame-difference method and background subtraction

this paper presents a new moving object detection algorithm based on edge information of the three-frame-difference method combined with the background subtraction. Firstly

the current frame subtracts the previous frame and the next frame separately

the next frame subtracts the previous frame

their results are added together to get a gray-scale image.The result is divided by three. Secondly

the gray-scale image is translated into binary image after being judged by threshold. And then it will be dealt with a series of morphological processing.Thirdly

the Canny edge detection operator is introduced into the middle frame

then the two results "and" operation to get the edge of the moving object.Finally

the current frame image subtracts the background image to get another gray-scale image

in the same way

it is translated into binary image after being judged by threshold. Then the final two results "or" operation. The moving object is got by doing a series of morphological processing. The experimental results show that

target detection rate by using the method increased by 9.7%~72.1%

and the error detection rate is reduced by 0.090%~2.900%. The combination of the two compared with the single detection algorithm can effectively and reliably detect the moving targets.

关键词

Keywords

references

赵春晖,潘泉,梁彦,等.视频图像运动目标分析[M].北京:国防工业出版社,2011.

ZHAO C H, PAN Q, LIANG Y,et al. Video Imagery Moving Targets Analysis[M]. Beijing:National Defense Industry Press, 2011. (in Chinese)

余启明. 基于背景减法和帧差法的运动目标检测算法研究[D].赣州:江西理工大学,2013.

YU Q M.Research on moving object detection algorithm of background subtraction and frame difference method[D]. Ganzhou:Jiangxi University of Science and Technology, 2013. (in Chinese)

高勇钢. 改进帧差法和背景差法的多目标跟踪[J]. 巢湖学院学报,2013,15(6):14-16.

GAO Y G. Multiple target tracking on improved frame difference and background difference method[J]. Journal of Chaohu College, 2013, 15(6):14-16. (in Chinese)

陈国玉. 序列图像中运动目标检测算法研究[D].大连:大连理工大学,2010.

CHEN G Y.Research on algorithm of moving object detection in sequences image[D]. Dalian:Dalian University of Technology, 2010. (in Chinese)

REMAGNINO P, TAN T, BAKER K. Multi-agent visual surveillance of dynamic scenes[J]. Image and Vision Computing, 1998, 16(8):529-532.

高凯亮,覃团发,王逸之,等. 一种基于帧差法与背景减法的运动目标检测新方法[J]. 电讯技术,2011,51(10):86-91.

GAO K L,QIN T F, WANG Y Z, et al. A novel approach for moving objects detection based on frames subtraction and background subtraction[J]. Telecommunication Engineering, 2011, 51(10):86-91. (in Chinese)

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.

CHEN Y, REN K, GU G H,et al. Moving object detection based on improved single Gaussian background model[J]. Chinese Journal of Lasers, 2014, 41(11):1109002. (in Chinese)

魏晓慧,李良福,钱钧. 基于混合高斯模型的运动目标检测方法研究[J]. 应用光学, 2010,31(4):574-578.

WEI X H, LI L F, QIAN J. Moving object detection based on mixture Gaussian model[J]. Journal of Applied Optics, 2010, 31(4):574-578. (in Chinese)

吴君钦,刘昊,罗勇. 静态背景下的运动目标检测算法[J]. 液晶与显示, 2012,27(5):682-686.

WU J Q, LIU H, LUO Y. Algorithm of moving object detection in static background[J]. Chinese Journal of Liquid Crystals and Displays, 2012, 27(5):682-686. (in Chinese)

邵娜,应捷. 视频序列中运动目标检测与跟踪方法研究[J]. 光机电信息, 2009,26(2):31-34.

SHAO N,YING J. Research on the method of detecting and tracking moving target in video sequence[J]. OME Information, 2009, 26(2):31-34. (in Chinese)

王霏. 基于视频的运动目标检测算法研究[D].长春:吉林大学,2014.

WANG F.Research on moving target detection algorithm based on video[D]. Changchun:Jilin University, 2014. (in Chinese)

LIU G, NING S K, YOU Y G, et al. An improved moving objects detection algorithm[C]//Proceedings of the 2013 International Conference on Wavelet Analysis and Pattern Recognition. Tianjin:IEEE, 2013:14-17.

秦襄培.MATLAB图像处理与界面编程宝典[M].北京:电子工业出版社,2009.

QIN X P. MATLAB Image Processing and Interface Programing[M]. Beijing:Electronics Industry Press, 2009. (in Chinese)

赵小川.MATLAB图像处理——能力提高与应用案例[M].北京:北京航空航天大学出版社,2014.

ZHAO X C. MATLAB Image Processing[M]. Beijing:Beijing University of Aeronautics and Astronautics Press, 2014. (in Chinese)

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