一种基于马尔可夫随机场的运动目标检测算法

任明艺; 李晓峰; 李在铭

doi:doi:10.3969/j.issn.1003-501x.2010.04.002

光电工程, 2010, 37 (4): 8, 网络出版: 2010-06-13

一种基于马尔可夫随机场的运动目标检测算法

A Novel Method for Moving Object Detection Based on Markov Random Field

论文大纲

任明艺 ^*李晓峰李在铭

作者单位

电子科技大学通信与信息工程学院,成都 611731

运动目标检测马尔可夫随机场分水岭算法过分割 moving object detection Markov random field watershed algorithm over-segmentation

摘要

本文提出了一种基于马尔可夫随机场的运动目标检测算法。针对传统时间分割使用主观固定阈值的缺点,使用马尔可夫随机场模型对差分图像建模,并提出了一种新的模型阶次选择算法,以及一种可以加速收敛过程的随机场迭代算法。采用期望最大算法获取高斯分布参数并检测运动变化区域,利用形态学运算修正时间分割模板;空间分割部分提出了基于人眼视觉特征的改进分水岭算法,有效地解决了过分割问题;最后对时、空间分割结果进行信息融合处理,从而得到完整的运动目标。仿真实验结果证明了本文算法可以有效地分割视频运动目标。

Abstract

A novel method for detecting moving objects from video sequences is proposed based on Markov random field. In order to overcome the drawback of subjective fixed threshold of traditional temporal segmentation, the difference image is modeled by Markov random field. A novel method for deciding the model size and initial parameters of MRF, and a new iteration method for greatly accelerating the convergence are proposed. Then, the Expectation-Maximization (EM) algorithm is carried out to obtain the Gaussian parameters and temporal moving area is detected. The temporal segmentation is then amended by morphological operations. Considering the lack of traditional spatial segmentation algorithm of watershed, an improved watershed algorithm in accord with the human vision characteristics, which can restrain over-segmentation effectively, is proposed. The temporal and spatial information fusion is fulfilled by ratio operation, and the moving objects are obtained. The emulation experiments demonstrate the validity of the proposed algorithm.

参考文献

[1] . Video segmentation for content-based coding[J]. IEEE Transactions on Circuits and Systems for Video Technology, 1999, 9(8): 1190-1203.

[2] 张晓波,刘文耀,吕大伟. 基于时空信息的自动视频对象分割算法[J]. 光电子·激光,2008,19(3)：384-387.

ZHANG Xiao-bo,LIU Wen-yao,Lü Da-wei. Automatic video object segmentation algorithm based on spatio-temporal information [J]. Journal of Optoelectronics · Laser,2008,19(3)：384-387

[3] . Efficient spatio-temporal segmentation for extracting moving objects in video sequences[J]. IEEE Transactions on Consumer Electronics(S0098-3063), 2007, 54(3): 1161-1167.

[4] Elgammal Ahmed,Harwood David,Davis Larry. Non-parametric model for background subtraction [C]// Proceedings of the 6th European Conference on Computer Vision,Dublin, Ireland, June 26-July 1,2000. London,UK：Springer-Verlag,2000：751-767.

[5] 印勇,王亚飞. 基于空间邻域相关性的运动目标检测方法[J]. 光电工程,2009,36(2)：1-5.

YING Yong,WANG Ya-fei. Moving Object Detection Based on Spatial Local Correlation [J]. Opto-Electronic Engineering,2009,36(2)：1-5.

[6] . Optical flow 3D segmentation and Interpretation: a variational method with active curve evolution and level sets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 2006, 28(11): 1818-1829.

[7] . Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 1984, 6(3): 721-741.

[8] . The mean field theory in EM procedures for Markov random fields[J]. IEEE Transactions on Signal Processing(S1053-587X), 1992, 40(10): 2570-2583.

[9] . Model Based Clustering, Discriminant Analysis, and Density Estimation[J]. Journal of the American Statistical Association(S0162-1459), 2002, 97(458): 611-631.

[10] . Approximate Bayes Factors for Image Segmentation:The Pseudolikelihood Information Criterion (PLIC)[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 2002, 24(11): 1517-1520.

[11] . Watersheds in digital space: An efficient algorithms based on immersion simulation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 1991, 13(6): 583-598.

[12] REN Ming-yi,LI Xiao-feng,LI Zai-ming. An improved watershed transformation for image segmentation [C]// Proceedings of International Conference on Communications, Circuits and Systems, 2008 (ICCCAS 2008),Xiamen,China,May 25-27,2008：830-833.

[13] . Morphological gray scale reconstruction in image analysis: applications and efficient algorithms[J]. IEEE Transactions on Image Processing(S1057-7149), 1993, 2(2): 176-201.

[14] . Morphological multiscale segmentation for image coding[J]. Signal Processing(S0165-1684), 1994, 38(3): 359-386.

[15] . Extracting semantic video objects[J]. IEEE Computer Graphics and Applications(S0272-1716), 2001, 21(1): 48-55.

任明艺, 李晓峰, 李在铭. 一种基于马尔可夫随机场的运动目标检测算法[J]. 光电工程, 2010, 37(4): 8. REN Ming-yi, LI Xiao-feng, LI Zai-ming. A Novel Method for Moving Object Detection Based on Markov Random Field[J]. Opto-Electronic Engineering, 2010, 37(4): 8.

一种基于马尔可夫随机场的运动目标检测算法

关于本站 Cookie 的使用提示

全站搜索

一种基于马尔可夫随机场的运动目标检测算法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索