|
|
|
| Analysis of Robust Background Modeling Techniques for Different Information Levels |
| WANG Zhi-Ling1, ZHOU Lu-Ping1, CHEN Zong-Hai1,2 |
1.MOE-MS Key Laboratory of Multimedia Calculation and Communication, Department of Automation,
University of Science and Technology of China, Hefei 230027
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy
of Sciences, Beijing 100080 |
|
|
|
|
Abstract Background modeling is a critical element of detecting and tracking moving objects, and the robustness problem of the background model attracts more and more attention. Firstly, the requirements for robustness are analyzed by taking two aspects into account: different applications/environments and different modalities of sampling sets. Then, a review of current background modeling algorithms and systems is presented according to their information levels and performance. After analyzing these algorithms and comparing typical background modeling systems, several promising directions of background modeling are pointed out for future research.
|
|
Received: 17 July 2007
|
|
|
|
|
|
[1] Zivkovic Z, Heijden F. Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction. Pattern Recognition Letters, 2006, 27(7): 773-780
[2] Wang Hanzi, Suter D. A Novel Robust Statistical Method for Background Initialization and Visual Surveillance // Proc of the 7th Asian Conference on Computer Vision. Hyderabad, India, 2006, Ⅰ: 328-337
[3] Wang Jianyu, Chen Xilin, Gao Wan, et al. Adaptive Appearance Model Robust to Background Variations. Journal of Software, 2006, 17(5): 1001-1008 (in Chinese)
(王建宇,陈熙霖,高 文,等.背景变化鲁棒的自适应视觉跟踪目标模型.软件学报, 2006, 17(5): 1001-1008)
[4] Guha P, Palai D, Venkatest K S, et al. A Multiscale Co-Linearity Statistic Based Approach to Robust Background Modeling // Proc of the 7th Asian Conference on Computer Vision. Hyderabad, India, 2006, Ⅰ: 297-306
[5] 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
[6] Haritaoglu I, Harwood D, Davis L S. W- 4: Real-Time Surveillance of People and Their Activities. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 809-830
[7] Toyama K, Krumm J, Brumitt B, et al. Wallflower: Principles and Practice of Background Maintenance // Proc of the IEEE International Conference on Computer Vision. Kerkyra, Greece, 1999, Ⅰ: 255-261
[8] Rwren C, 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
[9] 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. Ft Collins, USA, 1999, Ⅱ: 246-252
[10] Magee D R. Tracking Multiple Vehicles Using Foreground, Background and Motion Models. Image and Vision Computing, 2004, 22(2): 143-155
[11] Hsu Y Z, Nagel H H, Reckers G. New Likelihood Test Methods for Change Detection in Image Sequences. Computer Vision Graphics and Image Processing, 1984, 26(1): 73-106
[12] Rittscher J, Kato J, Joga S, et al. A Probabilistic Background Model for Tracking // Proc of the 6th European Conference on Computer Vision. Hilton Head Island, USA, 2000, Ⅱ: 255-261
[13] Elgammal A, Duraiswami R, Harwood D, et al. Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance. Proc of the IEEE, 2002, 90(7): 1151-1163
[14] Tavakkoli A, Nicolescu M, Bebis G. Automatic Robust Background Modeling Using Multivariate Non-Parametric Kernel Density Estimation for Visual Surveillance // Georgo B, Richard B, Darko K, et al, eds. Advances in Visual Computing. Berlin, Germany: Springer-Verlag, 2005: 363-370
[15] Kim K, Chalidabhongse T H, Itarwood D, et al. Real-Time Foreground-Background Segmentation Using Codebook Model. Real-Time Imaging, 2005, 11(3): 172-185
[16] Russell D, Gong S. A Highly Efficient Block-Based Dynamic Background Model // Proc of the IEEE Conference on Advanced Video and Signal Based Surveillance. London, UK, 2005: 417-422
[17] Yang Guanglin, Kong Lingfu. Approach of Constructing Background Model Based on Image Blocks. Robot, 2007, 29(1): 29-34 (in Chinese)
(杨广林,孔令富.基于图像分映的背景模型构建方法.机器人, 2007, 29(1): 29-34)
[18] Zhang Hongxun, Xu De. Fusing Color and Texture Features for Background Model // Proc of the International Conference on Fuzzy Systems and Knowledge Discovery. Xi'an, China, 2006: 887-893
[19] Rymel J, Renno J, Greenhi U D, et al. Adaptive Eigen-Backgrounds for Object Detection // Proc of the International Conference on Image Processing. Singapore, Singapore, 2004, Ⅲ: 1847-1850
[20] Li Liguan, Huang Weimin, Gu I Y H, et al. Statistical Modeling of Complex Backgrounds for Foreground Object Detection. IEEE Trans on Image Processing, 2004, 13(11): 1459-1472 |
|
|
|
2009, Vol. 22 
