Moving Object Segmentation Using Improved Running Gaussian Average Background Model

Autor:	Yung-Yaw Chen, Shu-Te Su
Rok vydání:	2008
Předmět:	Background subtraction Segmentation-based object categorization business.industry Gaussian Scale-space segmentation Pattern recognition Image segmentation symbols.namesake symbols Segmentation Computer vision Noise (video) Artificial intelligence business Gaussian process Mathematics
Zdroj:	DICTA
DOI:	10.1109/dicta.2008.15
Popis:	Moving object segmentation using Improved Running Gaussian Average Background Model (IRGABM) is proposed in this paper. Background subtraction for a relatively static background is a popular method for moving object segmentation in image sequences. However, there are some problems for the background subtraction method, such as the varying luminance effect, the background updating problem, and the noise effect. IRGABM has the advantages of fast computational speed and low memory requirement. Our study also shows its improvements on the above-mentioned problems. For the purpose of moving object segmentation, background updating time, auto-thresholding and shadow detection are also discussed in this paper.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::088879e7d5342a1aff8469f232dfbd7a https://doi.org/10.1109/dicta.2008.15 Zobrazit plný text záznamu