Moving Object Segmentation Using Improved Running Gaussian Average Background Model

Authors:
Shu-Te Su;Yung-Yaw Chen
Affiliations:
-;-
Venue:
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Year:
2008

Citing 0
Cited 1

Enhancement of background subtraction techniques using a second derivative in gradient direction filter

Journal of Electrical and Computer Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.