基于改进高斯混合模型的运动物体的图像检测

传统的高斯混合模型在RGB色彩空间只对孤立像素建模,检测结果不够准确,存在拖影现象,检测到的运动物体内部容易出现空洞.针对这些问题,本文提出了一种改进的高斯混合模型.该方法从更符合人眼视觉特性的HSV色彩空间对中心像素和周边像素构成的向量进行建模,改善了原算法的性能;利用彩色分割算法提取连通区域,充分地利用了运动物体的彩色信息,并基于Phong物体光照模型进行了阴影抑制,提高了传统高斯混合模型检测的准确性.实验结果表明,与传统高斯混合模型相比,本算法能更精确地检测出运动物体,对光照变化和阴影具有鲁棒性.

Moving Target Detection Based on Improved Gaussian Mixture Model

The traditional Ganssian Mixture Model (GMM) is built based on every single pixel in RGB color space, which leads to inaccurate detection results, Wailing smear and inanition inside the moving objects. The improved GMM is built in HSV color space, which is fit for human visual system. Single pixel is taken place by a vector which is composed of central pixel itself and its neighbor pixels in order to improve the performance of the model. The connected area is extracted through color segmentation method in order to make full use of the color information. Finally, the shadow area is restrained by Phong's object lighting model. According to the results of experiments, the improved algorithm can detect moving objects much more precisely. Compared with the traditional GMM, it is robust to lighting changes and shadow.