基于随机游走和混合高斯模型的运动目标检测

Moving Target Detection Based on Random Walk and Gaussian Mixture Model

针对传统的混合高斯背景建模算法未考虑同一帧内相邻像素之间的联系而导致无法准确地捕捉到运动物体轮廓的问题,提出了一种将随机游走和混合高斯模型相结合的前景目标检测算法。该算法利用混合高斯模型对视频源图像进行背景建模,从而获得初始运动目标,应用随机游走算法的分割效果及处理时间来确定种子点数量,结合初始运动目标对种子点进行标记,采用随机游走算法对视频源图像进行分割,将所得到的分割目标再与初始运动目标进行"与"运算,通过形态学处理得到作为结果的运动目标。为验证所提出算法的有效性,基于Matlab对所选取的4段视频进行了仿真检测。验证实验结果表明,所提出的前景目标检测算法较好地解决了混合高斯算法所产生的边缘模糊问题,同时也明显降低了前景噪声。

Since the conventional Gaussian mixture background modeling algorithm does not consider the correlation between adjacent pixels in the same frame, which cannot accurately capture the contour of moving objects, a foreground target detection method combined with random walk and Gaussian mixture model has been proposed. The background of the video source image has been modeled with the mixed Gauss model firstly, and the initial moving object has been obtained. Then, the number of seed points is determined by analyzing the segmentation result and the processing time of the random walk algorithm. The seed points have been labeled with the initial moving object, and the video source image is segmented by random walk algorithm. The obtained segmentation target and the initial moving ob- ject have been computed with logic AND operation. The morphological processing has been carried out to get the final moving target. In order to verify the effectiveness of the proposed algorithm, the four selected sequences of videos have been tested by Matlab. The test re- suits show that the proposed method has solved the edge blurring problem of Ganssian mixture algorithm, and reduced the foreground noise.