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自适应多模快速背景差算法

DOI: 10.11834/jig.20080228

Keywords: 视频监控,背景差算法,混合高斯模型,快速算法

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Abstract:

在多高斯模型的基础上,从场景中模型分布不均匀性出发,提出了一种新的快速背景差算法。该算法针对混合高斯模型中固定模型数量不足的问题,建立了模型产生和退出的机制,使模型数量能够自动适应场景特点,实现了高斯模型的实时自适应分布,即提高了准确性又有效地减少了模型的总量;同时,针对混合高斯模型中计算量大的问题,对模型参数的计算进行了优化,将耗时的浮点运算转化为整型运算,减少了计算量;算法中引入了生存时间和模型重现频率的概念,通过对模型重现频率的限制有效抑制高频噪声。与混合高斯模型的实验结果对比说明,该快速算法保持了原算法的优点,执行速度提高1倍以上,检测结果准确,算法内存消耗小,前景轮廓清晰,抑制高频噪声的能力强,整体效果优于混合高斯模型的背景差算法。

References

[1]  Richefeu J,Manzanera A.A new hybrid differentia] Filter for motion detection[A].In:Proceedings of International Conference onComputer Vision and Graphics[C],Warsaw,Poland,2004:22~24.
[2]  Stauffer C,Grimson W E L.Adaptive background mixture models for real-time tracking[J].Computer Vision and Pattern Recognition,1999,2:246~252.
[3]  Oliver N M,Rosario B,Pentland A P.A bayesian computer vision system for modeling human interactions[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22:831~843.
[4]  Javod O,Shah M.KNIGHTM:A Real Time Surveillance System[OL].http://www.cs.ucf.edu/~vision/papers.
[5]  Javod O,Shah M.Tracking and object classification for automated surveillance[A].In:Proceedings of the Seventh European Conference on Computer Vision[C],Copenhagen,Denmark,2002,4:343~357.
[6]  Cuechiara R,Grana C,Piccardi M,et al.Detecting moving objects,ghosts and shadows in video streams[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1337~1342.
[7]  Cucchiara R,Grona C,Piccardi M,et al.Detecting objects,shadows and ghosts in video streams by exploiting color and motion information[A].In:Proceedings of IEEE International Conference on Image Analysis and processing[C],Palermo,Italy,2001:360~365.
[8]  Tian Y L,Lu M,Hampapur A.Robust and efficient foreground analysis for real-time video surveillance[A].In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C],San Diego,CA,USA,2005,1:1182~1187.
[9]  Manzanera A,Richefeu J.A robust and computationally efficient motion detection algorithm based on ∑-△ background estimation[A].In:Proceedings of Indian Conference on Computer Vision,Graphics and Image Processiag[C],Kolkata,India,2004:46~51.
[10]  Stauffer C,Grimson E.Learning patterns of activity using real-time trecking[J].IEEE Transactions on Pattern Recognition and Machine Intelligence,2000,22(8):747~757.
[11]  Elgsmmal A,Harwood D,Davis L.Non-parametric model for background subtraction[A].In:Proceedings of the 7th IEEE International Conference on Computer Vision Frame-Rate Workshop[C],Kerkyra,Greece,1999:246~252.
[12]  Haritaoglu I,Harwood D,Davis L S.W4:real-time surveillance of people and their activitiee[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):809~830.
[13]  Javod O,Shafique K,Shah M.A hierarchical approach to robust background subtraction using color and gradient information[A].In:Proceedings of IEEE Workshop on Motion and Video Computing[C],Washington,DC,USA,2002:22~27.
[14]  Javed O,Rasheed Z,Alatas O,et al.KNIGHTM:A real time surveillance system for multiple overlapping and non-overlapping cameras[A].In:Proceedings of IEEE International Conference on Multimedia and Expe[C],Baltimore,Maryland,USA,2003.

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