Principal Component Analysis Based Codebook Background Modeling Algorithm
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摘要: 混合高斯(Mixture of Gaussian, MOG)背景建模算法和Codebook背景建模算法被广泛应用于监控视频的运动目标检测问题,但 混合高斯的球体模型通常假设RGB三个分量是独立的, Codebook的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点,这 些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型,该模型克服了混合高斯球体模型和Codebook圆柱体模型假设的局限 性,同时利用主成分分析(Principal components analysis, PCA)方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook背景建模算法.实验表明,本文算法不仅能够更准确地描述背 景像素值在RGB空间中的分布特征,而且具有良好的鲁棒性.
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关键词:
- 混合高斯模型 /
- 运动目标检测 /
- Codebook算法 /
- 主成分分析
Abstract: The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid-based background model, which overcomes the MOG and codebook's limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness. -
[1] Heikkila J, Silven O. A real-time system for monitoring of cyclists and pedestrians. In: Proceedings of the 2nd IEEE Workshop on Visual Surveillance. Fort Collins, USA: IEEE, 1999. 74-81[2] Piccardi M. Background subtraction techniques: a review. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. The Hague, Netherlands: IEEE, 2004. 3099-3104[3] Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. In: Proceedings of the 6th European Conference on Computer Vision. Dublin, Ireland: Springer, 2000. 751-767[4] Wren C, Azarbayejani A, Darrell T, Pentland A. Pfinder: real-time tracking of the human body. 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. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE, 1999. 246-252[6] Bouwmans T, Baf F E, Vachon B. Background modeling using mixture of Gaussians for foreground detection—a survey. Recent Patents on Computer Science, 2008, 1(3): 219-237[7] Wang Yong-Zhong, Liang Yan, Pan Quan, Cheng Yong-Mei, Zhao Chun-Hui. Spatiotemporal background modeling based on adaptive mixture of Gaussians. Acta Automatica Sinica, 2009, 35(4): 371-378(王永忠, 梁彦, 潘泉, 程咏梅, 赵春晖. 基于自适应混合髙斯模型的时空背景建模. 自动化学报, 2009, 35(4): 371-378)[8] Kim K, Chalidabhongse T H, Harwood D, Davis L. Real-time foreground-background segmentation using code book model. Real-Time Imaging, 2005, 11(3): 172-185[9] Chalidabhongse T H, Kim K, Harwood D, Davis L. A perturbation method for evaluating background subtraction algorithms. In: Proceedings of the Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance. Nice, France: IEEE, 2003. 110-116[10] Wu M, Peng X. Spatio-temporal context for codebook-based dynamic background subtraction. AEU-International Journal of Electronics and Communications, 2010, 64(8): 739-747[11] Tu Q, Xu Y, Zhou M. Box-based codebook model for realtime objects detection. In: Proceedings of the 7th World Congress on Intelligent Control and Automation. Chongqing, China: IEEE, 2008. 7621-7625[12] Doshi A, Trivedi M. “Hybrid cone-cylinder” codebook model for foreground detection with shadow and highlight suppression. In: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance. Sydney, Australia: IEEE. 2006. 19-24[13] Toyama K, Krumm J, Brumitt B, Meyers B. Wallflower: principles and practice of background maintenance. In: Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE, 1999. 255-261
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