文献类型：期刊文献

英文题名：An improved gaussian mixture model method for moving object detection

作者：Dong, Weiwei[1]; Wang, Yujian[1]; Jing, Wenpeng[1]; Peng, Taoxin[2]

第一作者：Dong, Weiwei

通讯作者：Wang, Yujian

机构：[1] College of Information Technology, Beijing Union University, Beijing, 100101, China; [2] School of Computing, Edinburgh Napier University, China

第一机构：北京联合大学智慧城市学院

年份：2016

卷号：14

期号：3A

起止页码：115-123

外文期刊名：Telkomnika (Telecommunication Computing Electronics and Control)

收录：EI(收录号：20164903096039);Scopus(收录号：2-s2.0-84999672443)

基金：This work was financially supported by National Natural Science Foundation of China (NSFC) (No.61271369).

语种：英文

外文关键词：Gaussian distribution - Image segmentation - Iterative methods - Object recognition

摘要：Aiming at the shortcomings of Gaussian mixture model background method, a moving object detection method mixed with adaptive iterative block and interval frame difference method in the Gaussian mixture model is proposed. In this method, the video sequences are divided into different size pieces in order to reduce the amount of calculation of the algorithm. It not only effectively solves the problem that the traditional Gaussian mixture model algorithm cannot detect large and slow moving object accurately, but also solves empty and no connection problems due to the introduction of block thought. The experimental results show that the improved algorithm has faster processing speed, better effect and better environment adaptability compared with the background of the Gaussian mixture model method. And it can detect moving object more accurately and completely. ? 2016 Universitas Ahmad Dahlan. All rights reserved.