An Improved GMM based Video Foreground Separation

被引：0

作者：

Zhang, Caixia ^{[1
]}

Xu, Qingyang ^{[2
]}

机构：

[1] Weihai Vocat Coll, New Sci Tech Pk, Weihai 264210, Peoples R China

[2] Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China

来源：

PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019) | 2019年

关键词：

Surveillance video; Object detection; GMM; Mixed Gaussian model; Local image flow; GAUSSIAN MIXTURE MODEL;

D O I：

10.1109/ccdc.2019.8832566

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Surveillance video analysis has applied to kinds of areas. Mixed GMM is a traditional target separation method. In order to overcome the object detection performance under dynamic scene, an improved algorithm is adopted to improve the mixed Gaussian model algorithm. A different initialization and model parameter updating strategy are adopted to detect the object under dynamic scene. The experiments validate the effectiveness of the algorithm at the lakeside and geyser.

引用

页码：1371 / 1374

页数：4

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