针对视觉背景提取(Vibe)算法不能有效地去除目标阴影以及不能快速消除鬼影现象的缺点,提出了一种改进的YUV_Vibe融合算法。该方法通过扩大样本的邻域选取范围,从而有效避免了同一样本重复选取;将更新因子从16调整至4,且将样本更新个数变为2,提高背景更新速率,加快鬼影现象消除速率;将YUV颜色信息特征与Vibe相融合,消除了阴影影响;通过融合双模型的构建,有效地减少了阴影误检测率。通过视频数据集对算法进行实验论证,检测结果表明,改进了的YUV_Vibe融合算法在准确度与识别率上都有提高,且实验检测的结果更准确。

The visual background extraction (Vibe) algorithm cannot effectively remove the shadow of the target, and cannot quickly remove the ghost phenomenon. To address the shortcomings of Vibe, an improved YUV_Vibe fusion algorithm is proposed. The algorithm expands the value range of the sample field, which effectively avoids the repetitive selection of the same samples. The updating factor is adjusted from 16 to 4, and the number of sample updates is set at 2, which accelerates the update rate of the background to eliminate the rate of ghost detection. The fusion of the YUV color information features with the Vibe algorithm eliminates the influence of shadows. By constructing a double fusion model, the false detection rate of shadows is effectively reduced. The algorithm is experimentally applied to video datasets. The test results reveal that the improved YUV_Vibe fusion algorithm has improved the accuracy and recognition rate, and the experimental detection results are more accurate.