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基于OpenCV的运动目标检测软件实现 被引量:3

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摘要 OpenCV是一种基于开源发行的跨平台计算机视觉库,应用范围非常广。从运动目标检测角度出发,介绍了基本背景差分法检测运动目标的一般流程和当前经典的混合高斯模型(GMM)。采用基于OpenCV的方法软件实现GMM算法,提取运动目标,对该算法进行了评价。
出处 《软件导刊》 2015年第12期132-133,共2页 Software Guide
基金 湖北省高等学校大学生创新创业训练计划项目(201410528019) 湖北工程学院科学研究项目(201607)
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  • 1HA J E, LEE W H. Foreground objects delection using muhiple difference images [J 1. Optical Engineering, 2010,4,q ( 4 ) : 1-5.
  • 2赵旭东,刘鹏,唐降龙,刘家锋.一种适应户外光照变化的背景建模及目标检测方法[J].自动化学报,2011,37(8):915-922. 被引量:18
  • 3DESSAUER M P, DUA S. Optical flow object detection, motion es- timation,and tracking on moving vehicles using wavelet decomposi- tions EC]. In: The International Soeiety for ()ptical Engineering, Bellingham,WA, USA : SPIE, 2010.
  • 4STAUFFER C,GRIMSON W E L. Adaptive background mixture models [or real-time traekingEC. Proceedings of the Computer So- eiety Conference on Computer Vision and Pattern Recognition, 1999(2) :246-252.
  • 5. GOYETTE N, JODOIN P M, PORIKI.I F, et al. Changedetcction. net :a new change detection benehmark datasetC3, ln: IEEE Com- puter Society Conference on Computer Vision and Pattern Recogni- tion Workshops, Piscataway, NJ, USA : IEEE, 2012 : 1 8.
  • 6屠礼芬,仲思东,彭祺.自然场景下运动目标检测与阴影剔除方法[J].西安交通大学学报,2013,47(12):26-31. 被引量:6
  • 7陈炳文,王文伟,杨文英.基于图像块和边缘增强的运动目标检测[J].计算机工程,2010,36(17):192-194. 被引量:6

二级参考文献48

  • 1Chris S,Grimson W E L.Adaptive Background Mixture Models for Real-time Tracking[C] //Proc.of IEEE Conference on Computer Vision and Pattern Recognition.Fort Collins,CO,USA:[s.n.] ,1999.
  • 2Collins R T,Lipton A J,Kanade T.A System for Video Surveillance and Monitoring:VSAM Final Report[R].Pittsburgh,PA,USA:Carnegie Melton University,2000.
  • 3Deng Xiaoyu,Bu Jiajun,Yang Zhi,et al.A Block-based Background Model for Video Surveillance[C] //Proc.of IEEE International Conf.on Acoustics,Speech and Signal Processing.Las Vegas,USA:[s.n.] ,2008.
  • 4[著],孙卫东[译].图像处理技术手册.北京:科学出版社,2007.
  • 5Stauffer C, Grimson W E L. Adaptive background mixture models for realwtime tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pat- tern Recognition. Fort Collins, USA: IEEE, 1999. 246-252.
  • 6Elgammal A M, Harwood D, Davis L S. Non-parametric model for background substraction. In: Proceedings of the 6th European Conference on Computer Vision. London, UK: Springer-Verlag, 2000. 751-767.
  • 7Elgammal A, Duraiswami R, Harwood D, Davis L S. Back- ground and foreground modeling using nonparametric ker- nel density estimation for visual surveillance. Proceedings of IEEE, 2002, 90(7): 1151-1163.
  • 8Parag T, Elgammal A, Mittal A. A framework for feature selection for background subtraction. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE, 2006. 1916-1923.
  • 9Perez A, Larranaga P, Inza I. Bayesian classifiers based on kernel density estimation: flexible classifiers. International Journal of Approximate Reasoning, 2009, 50(2): 341-362.
  • 10Banerjee A, Burlina P. Efficient particle filtering via sparse kernel density estimation. IEEE Transactions on Image Pro- cessing, 2010, 19(9): 2480-2490.

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