Abstract The illumination variation, waving trees, rippling water and noise are the main problems for the establishing of background model of dynamic scene. Aiming at the problems, a dynamic background modeling method is proposed based on the weighted histogram. In the proposed method, a weighted histogram is firstly defined by fusing the local spatial correlation of the image sequence, and it is regarded as a feature to represent the dynamic scene. Then, a simple clustering criterion for weighted histogram is proposed, which is used to cluster features by calculating luminance and chrominance components of the weighted histogram separately. Compared with the MOG(Mixture Of Gaussians), SCBM(Standard Codebook Model), HSVCBM(HSV CodeBook Model)and WHM(Weighted Histogram Model), the experimental results on several standard test video sequences show that the proposed method has better adaptability to the dynamic scene.
[1] Wang Z L, Zhou L P, Chen Z H. Analysis of Robust Background Modeling Techniques for Different Information Levels. Pattern Recognition and Artificial Intelligence, 2009, 22(2): 240-245 (in Chinese) (王智灵,周露平,陈宗海.针对不同信息特征的鲁棒背景建模技术分析.模式识别与人工智能, 2009, 22(2): 240-245) [2] Wren C R, Azarbayejani A, Darrell T, et al. Pfinder: Real-Time Tracking of the Human Body. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780-785 [3] Horprasert T, Harwood D, Davis L S. A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection // Proc of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece, 1999: 1-19 [4] Stauffer C, Grimson W E L. Adaptive Background Mixture Models for Real-Time Tracking // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA, 1999: 2246-2252 [5] Zivkovic Z. Improved Adaptive Gaussian Mixture Model for Background Subtraction // Proc of the 17th International Conference on Pattern Recognition. Cambridge, UK, 2004, II: 28-31 [6] El Baf F, Bouwmans T, Vachon B. A Fuzzy Approach for Background Subtraction // Proc of the 15th IEEE International Conference on Image Processing. San Diego, USA, 2008: 2648-2651 [7] Chen Y T, Chen C S, Huang C R, et al. Efficient Hierarchical Method for Background Subtraction. Pattern Recognition, 2007, 40(10): 2706-2715 [8] Migdal J, Grimson W E L. Background Subtraction Using Markov Thresholds // Proc of the 7th IEEE Workshop on Application of Computer Vision. Breckenridge, USA, 2005, II: 58-65 [9] Wang Y Z, Liang Y, Pan Q, et al. Spatiotemporal Background Modeling Based on Adaptive Mixture of Gaussians. Acta Automatica Sinica, 2009, 35(4): 372-378 (in Chinese) (王永忠,梁 彦,潘 泉,等.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): 372-378) [10] Wu M J, Peng X R. Spatio-Temporal Context for Codebook-Based Dynamic Background Subtraction. AEU-International Journal of Electronics and Communications, 2010, 64(8): 739-747 [11] Chen G, Yu Z Z, Wen Q, et al. Improved Gaussian Mixture Model for Moving Object Detection // Proc of the 3rd International Conference on Artificial Intelligence and Computational Intelligence. Taiyuan, China, 2011: 179-186 [12] Yin Z Z, Collins R. Belief Propagation in a 3D Spatio-Temporal MRF for Moving Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA, 2007: 1-8 [13] Tao L M, Wang Q F, Di H J. Markov Random Field in Visual Information Processing. Journal of Image and Graphics, 2009, 14(9): 1705-1711 (in Chinese) (陶霖密,王奇凡,邸慧军.视觉信息处理中的马尔科夫随机场.中国图象图形学报, 2009, 14(9): 1705-1711) [14] Li S Z. Markov Random Field Models in Computer Vision // Proc of the 3th European Conference on Computer Vision. Stockholm, Sweden, 1994, II: 361-370 [15] Vezhnevets V, Sazonov V, Andreeva A. A Survey on Pixel-Based Skin Color Detection Techniques // Proc of the 13th International Conference of Computer Graphics and Visualization Graphicon. Moscow, Russia, 2004: 85-92 [16] Lee J S, Kuo Y M, Chung P C, et al. Naked Image Detection Based on Adaptive and Extensible Skin Color Model. Pattern Recognition, 2007, 40(8): 2261-2270 [17] Zhao C, Wang X G, Cham W K. Background Subtraction via Robust Dictionary Learning. EURASIP Journal on Image and Video Processing, 2011. DOI:10.1155/2011/972961 [18] Kim K, Chalidabhongse T H, Harwood D , et al. Real-Time Foreground-Background Segmentation Using Codebook Model. Real-Time Imaging, 2005, 11(3): 172-185 [19] Fang X Y, He B, Luo B. New Codebook Model Based on HSV Color Space. Journal of Computer Applications, 2011, 31(9): 2497-2501 (in Chinese) (方贤勇,贺 彪,罗 斌.一种基于HSV颜色空间的新码书模型.计算机应用, 2011, 31(9): 2497-2501) [20] Li X F, Mei Z H. An Algorithm of Background Extraction Based on Statistics of Histogram Combining with Multi-Frame Average. Journal of Nanjing University of Posts and Telecommunications: Natural Science, 2008, 28(6): 74-77 (in Chinese) (李晓飞,梅中辉. 一种基于直方图统计与多帧平均混合的背景提取算法.南京邮电大学学报:自然科学版, 2008, 28(6): 74-77) [21] Heikkila M, Pietikainen M. A Texture-Based Method for Modeling the Background and Detecting Moving Objects. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(4): 657-662
2015, Vol. 28 
