视频监视前景图像估计的盲源提取方法
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摘要
在视频图像运动检测的背景消减方法中,场景图像或帧可建模为前景图像和背景图像的叠加或线性混合。然而,实际中图像的背景和前景往往相关,常用的主成分分析和独立分量分析等方法难以实现准确提取。为此,将视频图像的前景提取建模为盲源提取问题,提出了一种基于均方交叉预测误差的盲源提取方法,可以从相关的源视频图像中提取期望的前景图像,并将该方法扩展应用于基于基本模型和特征背景模型的背景消减方案中。基于人工和实际视频的实验验证了盲源提取背景消减方法的可行性和有效性。
In video surveillance, one scene image/frame can be modeled as a superimposition or linear mixture of foreground visual contents and background contents. In the real world, however, the background and foreground are correlated to each other. Therefore, the foreground extraction cannot be well solved by the PCA(principle component analysis) and the ICA(independent component analysis) algorithms. The foreground extraction was modeled as a BSE(blind source extraction) problem. The MSCPE(mean square cross prediction error), one solution of BSE, was generalized to extract desired source signal which was correlated with other source signals. Then MSCPE BSE method was applied to the background subtraction schemes by using the basic model and eigen backgrounds method. Experimental results on artificial video shows the feasibility of MSCPE, and the real-world video experiments demonstrate its effectiveness.
In video surveillance, one scene image/frame can be modeled as a superimposition or linear mixture of foreground visual contents and background contents. In the real world, however, the background and foreground are correlated to each other. Therefore, the foreground extraction cannot be well solved by the PCA(principle component analysis) and the ICA(independent component analysis) algorithms. The foreground extraction was modeled as a BSE(blind source extraction) problem. The MSCPE(mean square cross prediction error), one solution of BSE, was generalized to extract desired source signal which was correlated with other source signals. Then MSCPE BSE method was applied to the background subtraction schemes by using the basic model and eigen backgrounds method. Experimental results on artificial video shows the feasibility of MSCPE, and the real-world video experiments demonstrate its effectiveness.
引文
[1] Benezeth Y, Jodoin P M, Emile B, et al. Comparative study of background subtraction algorithms [J]. Journal of Electronic Imaging, 2010, 19(3): 033003.
[2] Hu W M, Tan T N, Wang L, et al. A survey on visual surveillance of object motion and behaviors [J]. IEEE Transactions on System Man and Cybernetics Part C, 2004, 34(3): 334-352.
[3] Wren C R, Azarbayejani A, Darrell T, et al. Pfinder: real-time tracking of the human body [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780-785.
[4] Lee D S. Online adaptive Gaussian mixture learning for video applications [J]. Lecture Notes in Computer Science, 2004, 3247: 105-116.
[5] Stauffer C, Grimson W E L . Adaptive background mixture models for real-time tracking[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999: 246-252.
[6] Stauffer C, Grimson W E L. Learning patterns of activity using real-time tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757.
[7] Kaewtrakulpong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection[M]//Remagnino P, Jones G A, Paragios N, et al. Video-Based Surveillance Systems.USA:Springer,2002: 149-158.
[8] Lee D S. Effective Gaussian mixture learning for video background subtraction [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005, 27(5): 827-832.
[9] Li L, Huang W, Gu Y H, et al. Statistical modeling of complex backgrounds for foreground object detection [J]. IEEE Transactions on Image Processing, 2004, 13(11): 1459-1472.
[10] Elgammal A, Duraiswami R, Harwood D, et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance [J]. Proceedings of IEEE,2002, 90(7): 1151-1163.
[11] Elgammal A, Duraiswami R, Davis L. Efficient kernel density estimation suing the fast Gauss transform with applications to color modeling and tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(11): 1499-1504.
[12] Rai V K, Mohanty A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform [J]. Mechanical Systems & Signal Processing, 2007, 21(6): 2607-2615.
[13] Barnich O, Droogenbroeck M V. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.
[14] Cucchiara 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.
[15] 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(8): 831-843.
[16] Tsai D M, Lai S C. Independent component analysis-based background subtraction for indoor surveillance [J]. IEEE Transactions on Image Processing, 2009, 18(1): 158-167.
[17] Starck J L, Elad M, Donoho D. Image decomposition via the combination of sparse representations and variational approach[J]. IEEE Transactions on Image Processing, 2005, 14(10): 1570-1582.
[18] Bobin J, Starck J L, Fadili J M, et al. Sparsity and morphological diversity in blind source separation [J]. IEEE Transactions on Image Processing, 2007, 16(11): 2662-2674.
[19] Herring K T, Mueller A V, Staelin D H . Blind separation of noisy multivariate data using second-order statistics: remote-sensing applications [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(10): 3406-3415.
[20] Hyv?rinen A, Karhunen J, Oja E. Independent component analysis[M]. USA: John Wiley & Sons, 2001.
[21] Cichocki A, Amari S. Adaptive blind signal and image processing[M]. USA: John Wiley & Sons, 2003.
[22] Wang G, Rao N N, Shepherd S J, et al. Extraction of desired signal based on AR model with its application to atrial activity estimation in atrial fibrillation[J]. EURASIP Journal on Advances in Signal Processing, 2008, 2008(1): 1-9.
[23] Wang G,Li C G, Dong L. Noise estimation using mean square cross prediction error for speech enhancement[J]. IEEE Transactions on Circuits & Systems I Regular Papers, 2010, 57(7): 1489-1499.
[24] Barros A K, Cichocki A. Extraction of specific signals with temporal structure[J]. Neural Computation, 2001, 13(9): 1995-2003.
[25] Liu W, Mandic D P, Cichocki A. A class of novel blind source extraction algorithms based on a linear predictor[C]//Proceedings of IEEE International Symposium on Circuits & Systems, 2005.
[26] Liu W, Mandic D P, Cichocki A . Blind second-order source extraction of instantaneous noisy mixtures [J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2006, 53(9): 931-935.
[27] Yang Y, Ge S S, Lee T H, et al. Facial expression recognition and tracking for intelligent human-robot interaction [J]. Intelligent Service Robotics, 2008, 1(2): 143-157.
[28] Kodagoda K R S, Ge S S, Wijesoma W S, et al. IMMPDAF approach for road-boundary tracking [J]. IEEE Transactions on Vehicular Technology, 2007, 56(2): 478-486.
[29] Zhang B L, Zhang H, Ge S S. Face recognition by applying wavelet subband representation and kernel associative memory[J]. IEEE Transactions on Neural Networks, 2004, 15(1): 166-177.
[30] Ge S S, Guan F, Pan Y, et al. Neighborhood linear embedding for intrinsic structure discovery[J]. Machine Vision & Applications, 2010, 21(3): 391-401.
[31] Ge S S, Yang Y, Lee T H. Hand gesture recognition and tracking based on distributed locally linear embedding [J]. Image and Vision Computing, 2008, 26(12): 1607-1620.
[2] Hu W M, Tan T N, Wang L, et al. A survey on visual surveillance of object motion and behaviors [J]. IEEE Transactions on System Man and Cybernetics Part C, 2004, 34(3): 334-352.
[3] Wren C R, Azarbayejani A, Darrell T, et al. Pfinder: real-time tracking of the human body [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780-785.
[4] Lee D S. Online adaptive Gaussian mixture learning for video applications [J]. Lecture Notes in Computer Science, 2004, 3247: 105-116.
[5] Stauffer C, Grimson W E L . Adaptive background mixture models for real-time tracking[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999: 246-252.
[6] Stauffer C, Grimson W E L. Learning patterns of activity using real-time tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757.
[7] Kaewtrakulpong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection[M]//Remagnino P, Jones G A, Paragios N, et al. Video-Based Surveillance Systems.USA:Springer,2002: 149-158.
[8] Lee D S. Effective Gaussian mixture learning for video background subtraction [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005, 27(5): 827-832.
[9] Li L, Huang W, Gu Y H, et al. Statistical modeling of complex backgrounds for foreground object detection [J]. IEEE Transactions on Image Processing, 2004, 13(11): 1459-1472.
[10] Elgammal A, Duraiswami R, Harwood D, et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance [J]. Proceedings of IEEE,2002, 90(7): 1151-1163.
[11] Elgammal A, Duraiswami R, Davis L. Efficient kernel density estimation suing the fast Gauss transform with applications to color modeling and tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(11): 1499-1504.
[12] Rai V K, Mohanty A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform [J]. Mechanical Systems & Signal Processing, 2007, 21(6): 2607-2615.
[13] Barnich O, Droogenbroeck M V. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.
[14] Cucchiara 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.
[15] 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(8): 831-843.
[16] Tsai D M, Lai S C. Independent component analysis-based background subtraction for indoor surveillance [J]. IEEE Transactions on Image Processing, 2009, 18(1): 158-167.
[17] Starck J L, Elad M, Donoho D. Image decomposition via the combination of sparse representations and variational approach[J]. IEEE Transactions on Image Processing, 2005, 14(10): 1570-1582.
[18] Bobin J, Starck J L, Fadili J M, et al. Sparsity and morphological diversity in blind source separation [J]. IEEE Transactions on Image Processing, 2007, 16(11): 2662-2674.
[19] Herring K T, Mueller A V, Staelin D H . Blind separation of noisy multivariate data using second-order statistics: remote-sensing applications [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(10): 3406-3415.
[20] Hyv?rinen A, Karhunen J, Oja E. Independent component analysis[M]. USA: John Wiley & Sons, 2001.
[21] Cichocki A, Amari S. Adaptive blind signal and image processing[M]. USA: John Wiley & Sons, 2003.
[22] Wang G, Rao N N, Shepherd S J, et al. Extraction of desired signal based on AR model with its application to atrial activity estimation in atrial fibrillation[J]. EURASIP Journal on Advances in Signal Processing, 2008, 2008(1): 1-9.
[23] Wang G,Li C G, Dong L. Noise estimation using mean square cross prediction error for speech enhancement[J]. IEEE Transactions on Circuits & Systems I Regular Papers, 2010, 57(7): 1489-1499.
[24] Barros A K, Cichocki A. Extraction of specific signals with temporal structure[J]. Neural Computation, 2001, 13(9): 1995-2003.
[25] Liu W, Mandic D P, Cichocki A. A class of novel blind source extraction algorithms based on a linear predictor[C]//Proceedings of IEEE International Symposium on Circuits & Systems, 2005.
[26] Liu W, Mandic D P, Cichocki A . Blind second-order source extraction of instantaneous noisy mixtures [J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2006, 53(9): 931-935.
[27] Yang Y, Ge S S, Lee T H, et al. Facial expression recognition and tracking for intelligent human-robot interaction [J]. Intelligent Service Robotics, 2008, 1(2): 143-157.
[28] Kodagoda K R S, Ge S S, Wijesoma W S, et al. IMMPDAF approach for road-boundary tracking [J]. IEEE Transactions on Vehicular Technology, 2007, 56(2): 478-486.
[29] Zhang B L, Zhang H, Ge S S. Face recognition by applying wavelet subband representation and kernel associative memory[J]. IEEE Transactions on Neural Networks, 2004, 15(1): 166-177.
[30] Ge S S, Guan F, Pan Y, et al. Neighborhood linear embedding for intrinsic structure discovery[J]. Machine Vision & Applications, 2010, 21(3): 391-401.
[31] Ge S S, Yang Y, Lee T H. Hand gesture recognition and tracking based on distributed locally linear embedding [J]. Image and Vision Computing, 2008, 26(12): 1607-1620.