RESEARCH ARTICLE


Wavelet Multi-Scale Transform Based Foreground Segmentation and Shadow Elimination



Ye-Peng Guan*, 1, 2
1 School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
2 Key Laboratory of Advanced Displays and System Application, Ministry of Education, 149 Yanchang Rd., Shanghai 200072, China

Article Information


Identifiers and Pagination:

Year: 2008
Volume: 1
First Page: 1
Last Page: 6
Publisher Id: TOSIGPJ-1-1
DOI: 10.2174/1876825300801010001

Article History:

Received Date: 11/10/2008
Revision Received Date: 26/10/2008
Acceptance Date: 30/10/2008
Electronic publication date: 28/11/2008
Collection year: 2008

© 2008 Ye-Peng Guan.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Communication and Information Engineering, Shanghai University; Key Laboratory of Advanced Displays and System Application, Ministry of Education, 149 Yanchang Rd., Shanghai 200072, China; Tel: +86 21 56331967; Fax: +86 21 56336908; E-mail: ypguan@shu.edu.cn


Abstract

An algorithm using wavelet multi-scale transform for segmenting foreground moving objects and suppressing shadow is proposed. The optimal selection of threshold is automatically determined which does not require any complex supervised training, manual calibration or hypothesis. The proposed algorithm is efficient enough to segment foreground moving objects with low contrast against the background. The reference image is used to extract foreground no matter the objects enter the field of view before captured or not. The developed method is highly computationally cost-effective since it does not concern with complex computation model, color model or background statistics at a time. By comparisons, it has been shown that the proposed approach is more robust and efficient to detect foreground and suppress shadow during coping with different indoors or outdoors circumstances.

Keywords: Wavelet multi-scale transformation, Foreground segmentation, Shadow suppression, Background subtraction, Threshold.