Memory-based Gaussian Mixture Modeling for Moving Object Detection in Indoor Scene with Sudden Partial Changes

摘要：

In this paper, a memory-based Gaussian Mixture Model (MGMM) is proposed inspired by the way human perceives the environment. The human memory mechanism is introduced to model the background, which can make the model remember what the scene has ever been and help the model adapt to the variation of the scene more quickly. Experimental results show the effect of the memory mechanism in segmenting moving objects with sudden partial changes in the background scene.

关键词： GMM MGMM backgound modeling backgournd subtract human memory

作者: Yujuan Qi Yanjiang Wang

作者单位: College of Information and Control Engineering China University of Petroleum,Dongying, Shandong, 257061, P.R.China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 752-755

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）