1.College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018 2.School of Computer, Zhejiang Industry Polytechnic College, Shaoxing 312000 3.College of Software, Zhejiang University of Technology, Hangzhou 310027
Abstract A random ferns based method is proposed for bilayer video segmentation with the capability of segmenting monocular video automatically. Motion feature dictionary is constructed by clustering the motion features of the video, and the motion features are modeled by random ferns. The video colors, motion features and neighboring relationships are constrained by using conditional random fields. The graph-cut algorithm is adopted for solving globally optimal segmentation results. The experimental results demonstrate the validity of the proposed algorithm, and the results of the proposed method are compared with other algorithms on different video data.
[1] Ridder C, Munkelt O, Kirchner H. Adaptive Background Estimation and Foreground Detection Using Kalman Filtering // Proc of the International Conference on Recent Advances in Mechatronics. Istanbul, Turkey, 1995: 193-199 [2] Toyama K, Krumm J, Brummit B, et al. Wallflower: Principles and Practice of Background Maintenance // Proc of the International Conference on Computer Vision. Corfu, Greece, 1999, Ⅰ: 255-261 [3] Yang Tao, Li S Z, Pan Quan, et al. Real-Time and Accurate Segmentation of Moving Objects in Dynamic Scene // Proc of the 2nd ACM International Workshop on Video Surveillance and Sensor Networks. New York, USA, 2004: 136-143 [4] Stauffer C, Grimson W E L. Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757 [5] Stenger B, Ramesh V, Paragios N, et al. Topology Free Hidden Markov Models: Application to Background Modeling // Proc of the International Conference on Computer Vision. Vancouver, Canada, 2001, Ⅰ: 294-301 [6] 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 [7] Chen Rui, Deng Yu, Xiang Shimin, et al. A Non-Parametric Foreground / Background Segmentation Method by Fusion of Intensity and Edge Feature. Journal of Computer-Aided Design & Computer Graphics, 2005, 17(6): 1278-1284 (in Chinese) (陈 睿,邓 宇,向世明,等.结合强度和边界信息的非参数前景/背景分割方法.计算机辅助设计与图形学学报, 2005, 17(6): 1278-1284) [8] Migdal J, Grimson E. Background Subtraction Using Markov Thresholds // Proc of the IEEE Workshop on Motion and Video Computing. Breckenridge, USA, 2005: 58-65 [9] Xu Wei, Zhou Yue, Gong Yihong, et al. Background Modeling Using Time Dependent Markov Random Field with Image Pyramid [EB/OL]. [2005- 01- 31]. http://www.soe.ucsc.edu/~tao/pps/Motion95.pdf [10] Yaser S, Mubarak S. Bayesian Modeling of Dynamic Scenes for Object Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1778-1792 [11] Wang Yang, Ji Qiang. A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, Ⅰ: 264-270 [12] Criminisi A, Cross G, Blake A, et al. Bilayer Segmentation of Live Video // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA, 2006: 53-60 [13] Yin Pei, Criminisi A, Winn J, et al. Tree-Based Classifiers for Bilayer Video Segmentation // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Minneapolis, USA, 2007: 1-8 [14] Friedman J, Hastie T, Tibshirani R. Additive Logistic Regression: A Statistical View of Boosting. Annals of Statistics, 2000, 28(2): 337-407 [15] Ozuysal M, Fua P, Lepetit V. Fast Keypoint Recognition in Ten Lines of Code // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Minneapolis, USA, 2007: 9-12 [16] Lepetit V, Fua P. Keypoint Recognition Using Randomized Trees. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1465-1479 [17] Bosch A, Zisserman A, Muoz X. Image Classification Using Random Forests and Ferns // Proc of the IEEE 11th International Conference on Computer Vision. Riode Janeiro, Brazil, 2007: 1-8 [18] Shotton J, Winn J, Rother C, et al. TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation // Proc of the 9th European Conference on Computer Vision. Graz, Austria, 2006, Ⅰ: 1-15 [19] Viola P, Jones M. Robust Real-Time Object Detection. International Journal of Computer Vision, 2004, 57(2): 137-154 [20] Boykov Y, Veksler O, Zabih R. Fast Approximate Energy Minimization via Graph Cuts. IEEE Trans on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239
2009, Vol. 22 
