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[1] Yang Xin, , Liu Jia, et al. Adaptive moving target detection algorithmbased on Gaussian mixture model [J]. Journal of Southeast University (English Edition), 2013, 29 (4): 379-383. [doi:10.3969/j.issn.1003-7985.2013.04.005]
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Adaptive moving target detection algorithmbased on Gaussian mixture model()
一种自适应的基于混合高斯模型的运动目标检测算法
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
29
Issue:
2013 4
Page:
379-383
Research Field:
Computer Science and Engineering
Publishing date:
2013-12-20

Info

Title:
Adaptive moving target detection algorithmbased on Gaussian mixture model
一种自适应的基于混合高斯模型的运动目标检测算法
Author(s):
Yang Xin1;  2;  3;  Liu Jia1;  Fei Shumin2;  Zhou Dake1
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2School of Automation, Southeast University, Nanjing 210096, China
3Key Laboratory of Photoelectric Control Technology, Luoyang 471000, China
杨欣1;  2;  3;  刘加1;  费树岷2;  周大可1
1南京航空航天大学自动化学院, 南京 210016; 2东南大学自动化学院, 南京 210096; 3光电控制技术重点实验室, 洛阳 471000
Keywords:
moving target detection;  Gaussian mixture model;  background subtraction;  adaptive method
运动目标检测;  高斯混合模型;  背景差分;  自适应方法
PACS:
TP391.41
DOI:
10.3969/j.issn.1003-7985.2013.04.005
Abstract:
In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image’s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.
为提高运动目标检测的可靠性, 提出了一种自适应的基于混合高斯模型的运动目标检测算法.该算法利用混合高斯分布对每个背景像素建模, 高斯分布的个数不是固定不变的, 而是随着像素值的混乱程度自适应变化.差分图像的像素按大小被分为2部分, 然后对这2部分分别进行自适应阈值化分割, 得到前景图像.利用基于形态学重构的阴影消除方法来改善前景图像分割的性能.不同实际场景的实验结果表明该算法能够快速准确地建立背景模型, 且具有更强的鲁棒性.

References:

[1] Zhang J Y, Barron J L. Optical flow at occlusion[C]//The Ninth Conference on Computer and Robot Vision. Toronto, Canada, 2012: 198-205.
[2] 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.
[3] Piccardi M. Background subtraction techniques: a review[C]//IEEE International Conference on Systems, Man and Cybemeties. Sydney, Australia, 2004, 4: 3099-3104.
[4] Wren C R, Azarbayejani A, Darrell A, et al. Pfinder: real-time tracking of the human body [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780-785.
[5] Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking[C]//IEEE Conference on Computer Vision and Pattern Recognition. Fort Collins, USA, 1999: 246-250.
[6] Zhang J, Chen C H. Moving objects detection and segmentation in dynamic video backgrounds[C]//IEEE Conference on Technologies for Homeland Security. Woburn, MA, USA, 2007: 64-69.
[7] Lee D S. Effective Gaussians mixture learning for video background subtraction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 827-832.
[8] Li G, Zeng R L, Lin L. Moving target detection in video monitoring system[C]//Proceedings of the Sixth World Congress on Intelligent Control and Automation. Dalian, China, 2006: 9778-9781.
[9] Xia Y Q, Ning S H, Shen H. Moving targets detection algorithm based on background subtraction and frames subtraction[C]//IEEE International Conference on Industrial Mechatronics and Automation. Wuhan, China, 2010: 122-125.
[10] Aboueldahab T, Fakhreldin M. Adaptive control of dynamic nonlinear systems using sigmoid diagonal recurrent neural network[C]//IEEE International Conference on Systems, Man and Cybernetics. Istanbul, Turkey, 2010: 4341-4345.

Memo

Memo:
Biography: Yang Xin(1978—), male, doctor, associate professor, yangxin@nuaa.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61172135, 61101198), the Aeronautical Foundation of China(No.20115152026).
Citation: Yang Xin, Liu Jia, Fei Shumin, et al. Adaptive moving target detection algorithm based on Gaussian mixture model[J].Journal of Southeast University(English Edition), 2013, 29(4):379-383.[doi:10.3969/j.issn.1003-7985.2013.04.005]
Last Update: 2013-12-20