A background modeling method for videos based on weighted statistical classification

In the field of intelligent video surveillance, foreground detection, moving target tracking and target recognition are the key technologies. They play an important role in target behavior analysis and understanding. In this paper a background modeling method based on weighted statistical classifica...

Full description

Saved in:
Bibliographic Details
Main Authors: Jiang-Qin Gui, Jian-Wei Zhang, Li-Qiang Hu, Ye Wen-Zhong, Yong-Hui Li, Dong-Fa Gao
Format: Conference Proceeding
Language:English
Subjects:
Abstracts
background modeling
Foreground detection
Gaussian mixture model
no-parameter background modeling
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the field of intelligent video surveillance, foreground detection, moving target tracking and target recognition are the key technologies. They play an important role in target behavior analysis and understanding. In this paper a background modeling method based on weighted statistical classification is proposed. As a non-parametric background model, it uses several state categories to express multiple states of a background pixel. It does not require the background pixels to obey Gaussian distribution and needs no training. The weights are updated according to the matching history of the background pixel. The background state is determined by a threshold. Experiment results show that it obtains excellent detection results and real-time detection speed in complex scenes.
ISSN:2160-133X
DOI:10.1109/ICMLC.2013.6890508