基于YCbCr的自适应混合高斯模型背景建模

混合高斯模型是最常用的背景建模方法之一,但是它的精确度是以耗时为代价的,且它在RGB颜色空间进行背景建模时,对噪声的处理效果一般。因此,对混合高斯模型进行改进,提出了一种基于YCbCr的自适应混合高斯模型背景建模方法。首先,将建模颜色空间从RGB转换到YCbCr;然后,采用自适应选择策略来确定混合高斯模型的高斯成分个数;最后,将高斯成分按照关键字的值进行排序,以确定背景模型。将提出的建模方法应用于运动目标检测,实验结果表明,提出的方法与混合高斯模型背景建模相比,运动目标检测的检测结果更准确,耗时更少。

An adaptive MoG background modeling based on YCbCr

Mixture of Gaussians (MoG) is one of the most common background modeling methods, but its accuracy comes at the expense of time, and the effect of noise treatment is general when the background is modeled in RGB color space. To improve MoG, we propose an adaptive MoG background modeling method based on YCbCr. First of all, the modeling color space is converted from RGB into YCbCr. Secondly, adaptive selection strategy is used to determine the number of gaussian components of the MoG. Finally, we order the gaussian components according to the value of sorting key words to determine the background model. The proposed modeling method is applied to moving objects detection experiments, and the experimental results show that the proposed approach is more accurate and less time-consuming in detecting moving objects compared to MoG background modeling.