基于边缘前景的混合高斯模型目标检测

混合高斯模型已经广泛应用于背景建模中。但是检测结果受到噪音的干扰和突变光照的影响。为了解决这个问题，将Stauffer的混合高斯模型进行改进并与边缘信息相结合。当三帧差分判断出场景变化时，像素点的学习率会自适应变化。用这种改进的混合高斯模型来获取运动物体的边缘图像和前景图像。对边缘图像进行图像膨胀，再与前景图像进行与运算，通过光流信息来填补空洞部分，得到最后的结果。实验结果表明，可以很好地去除噪音和解决光照突变的影响，提高了目标检测的效果，比传统方法更加有效。

Gaussian mixture model moving object detection based on foreground of edge images

Gaussian mixture model has been widely used for background modeling. However, the detection result is easily affected by noise and illumination mutation. In order to solve this problem, this paper proposes to combine the improved Gaussian mixture model with edge information. Once the method of three frame difference detects changes of the environment, the learning rate will be adjusted adaptively. The improved Gaussian mixture model is applied to extracting edge images and foreground images of moving objects. After dilating edge images, the result is obtained by computing the intersection of edge images and foreground images, and filling the hollow part based on the information of optical flow. Experimental results indicate that the proposed method has great capacity in restraining noise and dealing with illumination mutation, and improves the performance of object detection. It is more efficient than the traditional method.