摘要
针对运动检测算法中目标空洞和环境噪声难以消除等问题,提出一种背景建模算法。将待处理图像通过三层高斯模糊滤镜,分层抽取图像信息,通过分层建立分裂高斯混合模型,进行建模并计算运动区域。将上层提取的低频信息以及建模后提取的粗块化运动目标,加入到下层的背景判定计算流程中,根据综合判定结果纠正下层错误的模型参数。在公共数据库中的实验结果表明,该算法在高效地去除了环境噪声的情况下,可保证所提取运动目标的准确性,并且对光照突变不敏感,有较好的检测率和较低的误检率。
Aiming at empty target and ambient noise,a background modeling algorithm is proposed. The image to be processed is filtered by three Gaussian blur filters in order to extract information stratified. Then,each layer builds splitting mixture Gaussian model group and computes change area. During processing,low-frequency information extracted from the upper layer is added to the modeling and gets contours of moving target w hich w ill join to the calculation of low er layer. The result tested on public video dataset indicates that the method ensures the accuracy of the extracted moving target w hen environmental noise is removed clearly. The method reduces the algorithm sensitivity for the scene illumination change and has a higher detection rate and low er false detection rate.
出处
《计算机工程》
CAS
CSCD
北大核心
2015年第4期190-194,共5页
Computer Engineering
关键词
运动检测
背景建模
高斯混合模型
背景差法
分裂模型
噪声消除
motion detection
background modeling
Gaussian Mixture Model(GMM)
background subtraction method
splitting model
noise cancellation