基于自适应阈值的运动目标检测方法

Moving target detection method based on adaptive threshold

针对混合高斯背景减除法在运动目标检测应用中存在的不足,进行了以下两个方面的改进:第一,通过在混合高斯模型匹配中引入自适应匹配阈值的方法,解决由噪声或光照引起的误判问题;第二,在模型学习方面,采用不同的权重学习速率以检测静态背景区域,并提高模型的自适应性。实验结果表明,与传统的混合高斯模型的运动目标检测方法相比,改进后的方法在背景误判、场景适应性方面都有所改善。

This paper makes improvements on moving target detection method which is based on mixture Gaussian mod-el, specifically in two areas：the use of adaptive matching threshold solves the problem of misdetection of moving targets caused by noise or illumination change;and in terms of model learning, it uses different learning rate to detect static back-ground areas, improving the adaptiveness of model. Compared to the moving targets detection approach based on conven-tional mixture Gaussian model, the improved methods have significantly solved the problems of misdetection of moving object, and the adaptiveness of model.