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一种多区域视频监控入侵检测报警方法的研究 被引量:1

Research on an intrusion detection alarm method for multizone video monitoring
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摘要 研究一种用于多区域视频监控的智能入侵报警方法。该方法以多线程技术为基础,采用基于混合高斯背景建模的移动侦测算法实现多个区域实时入侵报警;采用视频监控卡自带函数库实现监控视频的显示。提出了剪贴板完成视频数据流的截取和数据格式的转换,避免了硬盘的反复读写,提高了监控画质和程序运行速度。最后给出了实验结果,结果表明该方法能有效的对入侵目标进行检测报警,且误报率较低,为1%~2%。 In this article, an inteligent intrusion alarm method for muhizone video monitoring was explored. The method which is based on multithread technology adopted the motion detection algorithm which is based on mixed Gaussian background modeling to realise real-time muhizone intrusion alarm; the embedded function library of the video monitoring card was adopted to display the monitoring video;in the paper the interception of video data flow and conversion of data format were finished through clipboard, repeated reading and writing of the hard disk were avoided, the monitoring picture quality and the processing speed of the program were improved. At last the experiment' s result was got,and the result shows that the method can do detection alarm to intrusion object with high effeeiency and low false alarm rate,between 1%-2%.
出处 《电子设计工程》 2010年第12期146-148,共3页 Electronic Design Engineering
关键词 视频监控 多线程 移动侦测 剪贴板 video monitoring multithread motion detection clipboard
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  • 1Kruglinski D 潘爱民 等.Visual C++技术内幕[M].北京:清华大学出版社,1999..
  • 2Kilger M.A shadow handler in a video-based real-time traffic monitoring system[A].In:Proceedings of IEEE Workshop on Applications of Computer Vision[C],Palm Springs,CA,USA,1992:1060 ~ 1066.
  • 3Elgammal A.Background and foreground modeling using nonparametric kernel density estimation for visual surveillance[J].Proceedings of IEEE,2002,90 (7):1151 ~ 1163.
  • 4Friedman N,Russell S.Image segmentation in video sequences:A probabilistic approach[A].In:Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence[C],Rhode Island,USA,1997:175 ~ 181.
  • 5Grimson W,Stauffer C,Romano R.Using adaptive tracking to classify and monitor activities in a site[A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C],Santa Barbara,CA,USA,1998:22 ~29.
  • 6Stauffer C,Grimson W.Adaptive background mixture models for realtime tracking[A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C],Fort Collins,Colorado,USA,1999,2:246~252.
  • 7Gao X,Boult T,Coetzee F,et al.Error analysis of background adaption[A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C],Hilton Head Isand,SC,USA,2000:503 ~510.
  • 8Power P W,Schoonees J A.Understanding background mixture models for foreground segmentation[A].In:Proceedings of Image and Vision Computing[C],Auckland,New Zealand,2002:267 ~271.
  • 9Lee D S,Hull J,Erol B.A Bayesian framework for gaussian mixture background modeling[A].In:Proceedings of IEEE International Conference on Image Processing[C],Barcelona,Spain,2003:973 ~ 976.
  • 10Rittscher J,Kato J,Joga S,et al.A probabilistic background model for tracking[A].In:Proceedings of European Conference on Computer Vision[C],Dublin,Ireland,2000,2:336 ~ 350.

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