An Efficient Foreground Object Detection Method Using a Color Cluster-Based Background Modeling Algorithm

被引：0

作者：

Tsai, Wen-Kai ^{[1
]}

Shue, Ming-Hwa ^{[2
]}

机构：

[1] Natl Formosa Univ, Dept Elect Engn, Huwei Township, Yunlin, Taiwan

[2] Natl Yunlin Univ Sci & Technol, Dept Elect Engn, Touliu 64002, Yunlin, Taiwan

来源：

2016 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C) | 2016年

关键词：

cluster color; pixel distribution;

D O I：

10.1109/IS3C.2016.188

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents a block-based color cluster background modeling and a foreground detection algorithm that possesses efficient processing and low memory requirement in a complex scene. In training phase, the color cluster and pixel distribution line (PDL) are efficiently used to reduce the background information. In detection phase, we can extract the foreground objects precisely and fast in complex scene by comparing the color clusters and PDLs. In order to overcome the block effect, refinement procedure is proposed to identify pixel-based foreground objects. Experimental results indicate that the proposed approach has consumes 32.5% less memory and reduce total error pixels by at least 16.9% compared to other existing methods.

引用

页码：736 / 739

页数：4