LONG-TERM BACKGROUND MEMORY BASED ON GAUSSIAN MIXTURE MODEL

被引：0

作者：

Zhao, W. ^{[1
]}

Zhao, X. D. ^{[1
]}

Liu, W. M. ^{[1
]}

Tang, X. L. ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Comp Sci, Harbin 150001, Peoples R China

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (IEEE VCIP 2013) | 2013年

关键词：

Long-term background memory; Piecewise sequences; Gaussian mixture model; Background subtraction; Foreground detection;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper aims to present a long-term background memory framework, which is capable of memorizing long period background in video and rapidly adapting to the changes of background. Based on Gaussian mixture model (GMM), this framework enables an accurate identification of long period background appearances and presents a perfect solution to numerous typical problems on foreground detection. The experimental results with various benchmark sequences quantitatively and qualitatively demonstrate that the proposed algorithm outperforms many GMM-based methods for foreground detection, as well as other representative approaches.

引用

页数：5

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