计算机应用与软件Issue(1)：286-289,4.DOI:10.3969/j.issn.1000-386x.2014.01.077
基于改进混合高斯模型的自适应运动车辆检测算法
ADAPTIVE MOVING VEHICLE DETECTION ALGORITHM BASED ON IMPROVED GAUSSIAN MIXTURE MODEL
摘要
Abstract
There are often the cases in road video surveillance systems that the vehicles are slowly moving or in short stay.In view of the problems that the background subtraction method of traditional Gaussian mixture model is sensitive to abrupt changes in environment and has information loss on slow moving target,we propose an improved adaptive vehicle detection algorithm.First,in order to restrain the foreground of slow movement to be trained to the background,the present pixel-values are classified before updating the parameters,and the models are set different replacement rates according to classification results.Secondly,for removing the interference of environmental changes,a metric factor that tracks environmental changes is introduced to realise the adaptive switch between the background subtraction and the inter-frame difference algorithm when abrupt environmental change occurs.Finally the more accurate object is gotten by ecological filtering.Experiments show that this algorithm can get better detection effect for moving vehicles in daytime real-time traffic video.关键词
自适应/混合高斯模型/背景减除/目标检测Key words
Adaptive/Mixture/Gaussian model/Background subtraction/Target detection分类
信息技术与安全科学引用本文复制引用
张虎,方华,李春贵..基于改进混合高斯模型的自适应运动车辆检测算法[J].计算机应用与软件,2014,(1):286-289,4.基金项目
广西自然科学基金项目（2011GXN SFA018162）。 （）
