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. 基于自适应混合高斯模型的时空背景建模. 自动化学报, 2009, 35(4): 371-378)
[3]
Wren C R, Azarbayejani A, Darrell T, Pentland A P. Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780-785
[4]
Horprasert T, Harwood D, Davis L S. A statistical approach for real-time robust background subtraction and shadow detection. In: Proceedings of the 12th International Conference on Computer Vision. Kerkyra, Greece: IEEE, 1999. 1-19
[5]
Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of the 14th International Conference on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE, 1999. 246-252
[6]
Chen G, Yu Z Z, Wen Q, Yu Y Q. Improved Gaussian mixture model for moving object detection. Artificial Intelligence and Computational Intelligence, 2011, 7002: 179-186
[7]
Kim K, Chalidabhongse T H, Harwood D, Davis L. Real-time foreground-background segmentation using codebook model. Real-Time Imaging, 2005, 11(3): 172-185
[8]
Zhao Xu-Dong, Liu Peng, Tang Jiang-Long, Liu Jia-Feng. Background modeling adaptive to outdoor illumination variation and foreground detection approach. Acta Automatica Sinica, 2011, 37(8): 915-922(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. 自动化学报, 2011, 37(8): 915-922)
[9]
Yin Z Z, Collins R. Belief propagation in a 3D spatio-temporal MRF for moving object detection. In: Proceedings of the 23rd Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA: IEEE, 2007. 1-8
[10]
Migdal J, Grimson W E L. Background subtraction using Markov thresholds. In: Proceedings of the 7th Workshop on Application of Computer Vision. Breckenridge, USA: IEEE, 2005. 58-65
[11]
Wu M J, Peng X R. Spatio-temporal context for codebook-based dynamic background subtraction. AEU-International Journal of Electronics and Communication, 2010, 64(8): 739-747
[12]
Burghouts G J, Geusebroek J M. Performance evaluation of local colour invariants. Computer Vision and Image Understanding, 2009, 113(1): 48-62
[13]
Li S Z. Markov random field modeling in computer vision. In: Proceedings of the 3rd European Conference on Computer Vision. Secaucus, NJ: Springer-Verlag, 1995. 361-370
[14]
Besag J. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B (Methodological), 1974, 36(2): 192-236
[15]
Szeliski R, Zabih R, Scharstein D, Veksler O, Kolmogorov V, Agarwala A, Tappen M, Rother C. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(6): 1068-1080
[16]
Besag B J. On the statistical analysis of dirty picture. Journal of the Royal Statistical Society, Series B (Methodological), 1986, 48(3): 259-302
[17]
Felzenszwalb P F, Huttenlocher D P. Efficient belief propagation for early vision. International Journal of Computer Vision, 2006, 70(1): 41-54
[18]
Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239
[19]
Tao Lin-Mi, Wang Qi-Fan, Di Hui-Jun. Markov random field in visual information processing. Journal of Image and Graphics, 2009, 14(9): 1705-1711(陶霖密, 王奇凡, 邸慧军. 视觉信息处理中的马尔可夫随机场. 中国图象图形学报, 2009, 14(9): 1705-1711)
[20]
Heikkila M, Pietikainen M. A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 657-662
[21]
Chen Y T, Chen C S, Huang C R, Huang Y P. Efficient hierarchical method for background subtraction. Pattern Recognition, 2007, 40(10): 2706-2715
