针对视频背景建模和运动目标检测，本文提出一种基于分块模型更新的单高斯背景建模方法。新方法将视频图像划分为多个区块，并对块内的像素进行统一建模，以替代传统方法中对单像素点的建模。由于块像素的平均更符合高斯分布特性，有利于发挥单高斯建模方法的优势，因此增强了算法应对复杂背景的能力，同时分块建模也有效降低了算法复杂性。针对破碎团块的合并问题，本文提出一种“比对就近”的策略，将相隔距离在设定阈值内的团块合并为一个目标。实验结果验证了本文所提方法的有效性。%This paper presents a novel algorithm based on single Gaussian model for video background modeling and moving object detection,in which video image is divided into several blocks and pixels in each block are modeled together. Compared to traditional per-pixel background modeling method,the proposed block modeling method matches the character-istics of Gaussian distribution better and more conducive to exploiting the advantages of single Gaussian modeling method. So it enhances the capability to deal with complex background and effectively reduces the complexity in operation.Further-more,this paper proposes a new CMNO (Compare and Merge the Nearest Ones)strategy to handle the fragment problem in foreground so that the nearest broken agglomerate is merged to construct a new object.The experimental results have veri-fied the effectiveness of proposed method.
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