全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于判别模型的视频前景/阴影自动分割算法*

, PP. 849-855

Keywords: 视频分割,活动阴影模型,二维条件随机场

Full-Text   Cite this paper   Add to My Lib

Abstract:

活动阴影是影响视频目标分割准确性的重要因素,有效检测与消除活动阴影是视频分割的一大难题.本文提出一种基于判别模型的前景/阴影自动分割算法.它能在室内户外多种环境中对活动阴影进行检测和消除.算法在像素级别上对背景、阴影以及前景进行建模,利用二维条件随机场对这些分布模型进行约束,通过概率图模型推断算法求出全局最优的分割结果.在实验中采用各种环境的视频数据对本文算法的有效性进行测试,并与其他分割算法的结果进行比较,证明本文算法的误分率较低.

References

[1]  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
[2]  Stauffer C, Grimson W. Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757
[3]  Stenger B, Ramesh V, Paragios N, et al. Topology Free Hidden Markov Models: Application to Background Modeling // Proc of the 8th IEEE International Conference on Computer Vision. Vancouver, Canada, 2001, Ⅰ: 294-301
[4]  Martel-Brisson N, Zaccarin A. Moving Cast Shadow Detection from a Gaussian Mixture Shadow Model // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, Ⅱ: 643-648
[5]  Porikli F, Thornton J. Shadow Flow: A Recursive Method to Learn Moving Cast Shadows // Proc of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005, Ⅰ: 891-898
[6]  Yoneyama A, Yeh C H, Kuo C C J. Moving Cast Shadow Elimination for Robust Vehicle Extraction Based on 2D Joint Vehicle/Shadow Models // Proc of the IEEE Conference on Advanced Video and Signal Based Surveillance. Miami, USA, 2003: 229-236
[7]  Chen Baisheng, Lei Yunqi. Indoor and Outdoor People Detection and Shadow Suppression by Exploiting HSV Color Information // Proc of the 4th International Conference on Computer and Information Technology. Wuhan, China, 2004: 137-142
[8]  Salvador E, Cavallaro A, Ebrahimi T. Cast Shadow Segmentation Using Invariant Color Features. Computer Vision and Image Understanding, 2004, 95(2): 238-259
[9]  Zha Yufei, Chu Ying, Wang Xun, et al. A Boosting Discriminative Model for Moving Cast Shadow Detection. Chinese Journal of Computers, 2007, 30(8): 1295-1301 (in Chinese) (查宇飞,楚 瀛,王 勋,等. 一种基于Boosting判别模型的运动阴影检测方法.计算机学报, 2007, 30(8): 1295-1301)
[10]  Chu Yiping, Zhang Yin, Huang Yejue, et al. A Spatiotemporal Algorithm for Video Foreground and Shadow Segmentation. Pattern Recognition and Artificial Intelligence, 2008, 21(4): 546-551 (in Chinese) (褚一平,张 引,黄叶珏, 等.融合时空信息的前景/阴影视频分割算法.模式识别与人工智能, 2008, 21(4): 546-551)
[11]  Migdal J, Grimson E. Background Subtraction Using Markov Thresholds // Proc of the IEEE Workshop on Motion and Video Computing. Breckenridge, USA, 2005, Ⅱ: 58-65
[12]  Zhou Yue, Gong Yihong, Tao Hai. Background Modeling Using Time Dependent Markov Random Field with Image Pyramid // Proc of the IEEE Workshop on Motion and Video Computing. Breckenridge, USA, 2005, Ⅱ: 8-13
[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]  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)
[15]  Sheikh Y, Shah M. Bayesian Modeling of Dynamic Scenes for Object Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1778-1792
[16]  Lafferty J, McCallum A, Pereira F. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data // Proc of the 18th International Conference on Machine Learning. Williams College, USA, 2001: 282-289
[17]  Sha Fei, Pereira F. Shallow Parsing with Conditional Random Fields // Proc of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. Edmonton, Canada, 2003: 134-141
[18]  Kumar S, Hebert M. A Hierarchical Field Framework for Unified Context-Based Classification // Proc of the IEEE International Conference on Computer Vision. Beijing, China, 2005, Ⅱ: 1284-1291
[19]  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
[20]  Leone A, Distante C. Shadow Detection for Moving Objects Based on Texture Analysis. Pattern Recognition, 2007, 40(4): 1222-1233
[21]  Yedidia J S, Freeman W T, Weiss Y. Understanding Belief Propagation and Its Generalizations // Lakemeyer G, Nebel B, eds. Exploring Artificial Intelligence in the New Millennium. San Francisco, USA: Morgan Kaufmann, 2003: 239-269
[22]  Berthod M, Kato Z, Yu S, et al. Bayesian Image Classification Using Markov Random Fields. Image and Vision Computing, 1996, 14(4): 285-295

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133