Robust background subtraction and maintenance

被引：32

作者：

Zang, Q ^{[1
]}

Klette, R ^{[1
]}

机构：

[1] Univ Auckland, CITR Tamaki, Auckland, New Zealand

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2 | 2004年

关键词：

D O I：

10.1109/ICPR.2004.1334047

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Background subtraction is one of the main techniques to extract moving objects from background scenes. A mixture of Gaussians is a common model for background subtraction that has been used in many applications. However modelling background pixels using this model results into a low-level process at pixel level. Some of its main drawbacks are: a subtracted (moving object) region may contain holes; it can not solve partial occlusion problems, and it requires updates in cases of shadows or sudden changes in the scene. We present a multi-layered mixture of Gaussians model named PixelMap. We combine the mixture of Gaussians model with concepts defined by region level and frame level considerations. Our experimental results show that our method improved the accuracy of extracting moving objects from background. A single stationary camera has been used.

引用

页码：90 / 93

页数：4

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