Moving Objects Detection Based on Gaussian Mixture Model and Saliency Map

被引：1

作者：

Lin, Lili ^{[1
]}

Chen, Nengrong ^{[1
]}

机构：

[1] Zhejiang Gongshang Univ, Hangzhou, Zhejiang, Peoples R China

来源：

ADVANCED RESEARCH ON MECHANICAL ENGINEERING, INDUSTRY AND MANUFACTURING ENGINEERING, PTS 1 AND 2 | 2011年 / 63-64卷

关键词：

Moving objects detection; Gaussian mixture model; Salient region detection;

D O I：

10.4028/www.scientific.net/AMM.63-64.350

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The background modeling method based on the Gaussian mixture model (GMM) is usually used to detect the moving objects in static background. But when applied to dynamic background, for example caused by camera jitter, the wrong detection rate of moving objects is high, and thus affects the follow-up tracking. In addition, the method with GMM can not effectively remove the moving objects shadow region. This paper proposes a moving object detection method based on GMM and visual saliency maps, which not only can remove the disturbance caused by camera jitter, but also can effectively solve the shadow problem and achieve stable moving objects detection.

引用

页码：350 / 354

页数：5

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