一种混合高斯背景模型下的像素分类运动目标检测方法

运动目标检测在智能视频监控、人机交互、目标导航等诸多领域有着广泛应用.背景减法是运动目标检测中应用较广泛的一种方法.在该方法中,背景建模和阈值化分割是最重要的步骤,直接决定了检测效果的好坏.当目标本身变化比较大时,若利用传统的基于全局阈值的分割法,分割效果并不理想.针对基于全局阈值分割差分图像存在的问题,本文提出了一种基于混合高斯背景模型的像素分类运动目标检测方法.该方法首先利用混合高斯模型对背景建模,克服了场景变化等因素带来的影响;其次,利用背景减法得到差分图像并对像素进行分类,最后对分类后的像素集分别进行阈值化分割,得到前景目标.实验结果表明,与传统的基于全局阈值的分割法相比,本文算法能够获得更好的检测效果和鲁棒性.

Detection of moving objects using pixel classification based on Gaussian mixture model

Detection of moving objects(DMB) is widely used in many areas,such as intelligent video surveillance,human-machine interaction and target navigation.The background subtraction algorithm is widely used for DMB.In the traditional algorithm,background image is generated from the reference frame and is abstracted from the current frame to form differential image.Afterwards,the moving regions are separated from the differential image by predefined threshold.The procedure of generating background image is called ...