改进的自适应混合高斯前景检测方法

Improved object detection method of adaptive Gaussian mixture model

针对混合高斯背景模型计算量大、存在阴影和鬼影的不足,提出一种基于混合高斯模型的改进前景检测算法。通过分析背景的稳定性来选择连续或隔帧更新方式对背景模型中的参数进行更新,提高算法的运算速度。在背景更新方面,让更新率与权值相关联从而使更新率随权值改变并且对目标移动后显露的背景像素给予更大的更新率,提高背景的稳定性并解决鬼影现象及前景与背景转化的问题。对检测出的目标,用适应性更高的RGB颜色空间畸变模型进行阴影检测和消除,并进行高斯金字塔滤波和形态学滤波处理,以得到更好的前景目标。实验结果表明,该方法能提高算法的计算效率且准确地分割前景目标。

The deficiency of Gaussian Mixture Model （GMM） is of high computation cost and cannot deal with the shadow and ghosting. An improved foreground detection algorithm based on GMM was proposed in this paper. By analyzing the stability of the background, intermittent or continuous frame updating was chosen to update the parameters of the GMM. It can efficiently reduce the runtime of the algorithm. In the background updating, the updating rate was associated with the weight and this made it change with the weight. The background pixels which appeared after the objects moving were set a larger updating rate. It can improve the stability of the background and solve the problem of ghosting phenomenon and the transformation of background and foreground. After objects detection, the algorithm eliminated the shadow based on the RGB color space distortion model and treated the result by Gauss pyramid filtering and morphological filtering. Through the whole process, a better contour was obtained. The experimental results show that this algorithm improves the calculation efficiency and accurately segments the foreground object.