Online K-Means Algorithm for Background Subtraction

被引：0
|
作者：
Chen, Peng [1 ]
Jin, Beibei [1 ]
Zhu, Xiangbing [1 ]
Fang, Mingxing [1 ]
机构：
[1] Anhui Normal Univ, Coll Phys & Elect Informat, Wuhu 241000, Peoples R China
来源：
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON MECHATRONICS, MATERIALS, CHEMISTRY AND COMPUTER ENGINEERING 2015 (ICMMCCE 2015) | 2015年 / 39卷
关键词：
Gaussian mixture model; on-line K-means; background subtraction;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Background subtraction is an important step in video processing. GMM algorithm uses Gaussian mixture model to identify moving objects and efficient equations have been derived to update GMM parameters. In order to compute parameters more accurately while maintain constant computing time per frame, we apply online K-Means algorithm to update the parameters of Gaussian mixture models and the corresponding incremental K-means equations are derived. Experiments demonstrate that online K-means algorithm can give more efficient segment result than previous update equations.
引用
收藏
页码：681 / 685
页数：5
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