基于高斯混合模型的空间域背景分离法及阴影消除法

Space-domain Background Subtraction and Shadow Elimination Based on Gaussian Mixture Model

运动检测和背景分离技术是智能视频监控系统中的一项关键技术。由于目前广泛使用的高斯混合模型背景分离法是在像素域的时间尺度上对像素进行分类,因此常常造成误判,且无法解决阴影问题。为解决此问题,提出了一种空间域上的背景分离法。该方法首先将像素检测从像素域拓展至空间域的局部窗口内;然后在得到前景点集后,再将此空间域检测思想结合像素亮度特征运用到阴影消除中;最后,对经典模型的部分参数估计方法进行了修改。相关的实验结果证明,该方法可用于提高背景分离的检测精度和实现运动物体阴影消除。

Moving detection is a key technology in robust video surveillance. Currently widely used Gaussian mixture model （GMM） always detects incorrectly and cannot deal with shadows based on the pixel-level and time-domain classification, so we introduce an effective algorithm extending the pixel-level detectign to space-domaln detection with the combination of illumination of the pixel using GMM and apply it for shadow removal after the first step when foreground pixels has been got. Besides, some parameters in the standard GMM are modified. Experiments show that our algorithm is effective both on detect accuracy and shadow removal.