Video moving object segmentation is a basic problem of computer vision and video processing. In the video sequences of a dynamic scene which has global (camera) motion, accurate moving object segmentation is still a key and hot research topic. A novel video moving object segmentation algorithm based on global motion compensation and non-parametric kernel density estimation is proposed in this paper. Firstly, an efficient and accurate global motion compensation method is used to remove the motion of the background. Then the non-parametric kernel density estimation is applied to establish foreground/background probability models. Finally, the moving object can be obtained by comparing the foreground/background probability and morphological post-processing. The experimental results demonstrate that the proposed algorithm has good results and reduces the complexity of moving object segmentation in dynamic scenes.%视频运动对象分割是计算机视觉和视频处理的基本问题.在摄像机存在全局运动的动态场景下,准确分割运动对象依然是难点和热点问题.本文提出一种基于全局运动补偿和核密度检测的动态场景下视频运动对象分割算法.首先,提出匹配加权的全局运动估计补偿算法,消除动态场景下背景运动对运动对象分割的影响；其次,采用非参数核密度估计方法分别估计各像素属于前景与背景的概率密度,通过比较属于前景和属于背景的概率及形态学处理得到运动对象分割结果.实验结果证明,该方法实现简单,有效地提高了动态场景下运动对象分割的准确性.
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