Abstract: Aiming at the difficult problem of unsupervised or automatic foreground extraction, an automatic foreground extraction method based on genetic mechanism and difference of Gaussian, named GFO, was proposed. Firstly, Gaussian variation was used to extract the relative important regions in the image, which were defined as candidate seed foregrounds. Secondly, based on the edge information of the original image and the candidate seed foregrounds, the contour of foreground object contour was generated according to connectivity and convex sphere principle, called star convex contour. Thirdly, the adaptive function was constructed, the seed foreground was selected, and the genetic mechanism of selection, crossover and mutation was used to obtain the accurate and valid final foreground. The experimental results on the Achanta database and multiple videos show that the performance of the GFO method is superior to the existing automatic foreground extraction based on difference of Gaussian (FMDOG) method, and have achieved a good extraction effect in recognition accuracy, recall rate and F_β index.

Key words: image processing, video surveillance, foreground extraction, difference of Gaussian, genetic algorithm

摘要： 针对无监督或全自动前景提取这一技术难点问题，提出了一种基于遗传机制和高斯变差的自动前景提取（GFO）方法。首先，利用高斯变差提取图像中的相对重要区域，定义为候选种子前景；之后，利用原始图像和候选种子前景的边沿信息，根据连通性和凸球原则生成前景目标区域轮廓，称之为星凸轮廓；最后，构造适应性函数，选择种子前景，利用选择、交叉及变异的遗传机制，得到精确且有效的最终前景。在Achanta数据库和多个视频上的实验结果表明，GFO方法的性能优于已有的基于高斯变差的自动前景提取（FMDOG）方法，且在识别的准确率、召回率以及F_β指标上都取得了较好的抽取效果。

关键词: 图像处理, 视频监控, 前景提取, 高斯变差, 遗传算法