一种基于马尔可夫随机场的运动目标检测算法(英文)  

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

作　　者：任明艺[1] 李晓峰[1] 李在铭[1] 

机构地区：[1]电子科技大学通信与信息工程学院,成都611731

出　　处：《光电工程》2010年第4期8-14,共7页Opto-Electronic Engineering

基　　金：国家863高技术计划项目(2004AA8223120);国家自然科学基金资助项目(10376005)

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。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.

关 键 词：运动目标检测 马尔可夫随机场 分水岭算法 过分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]