Moving Object Segmentation Using Improved Running Gaussian Average Background Model

Author

Su, Shu-Te ; Chen, Yung-Yaw

Author_Institution

Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei

fYear

2008

Firstpage

24

Lastpage

31

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.

Keywords

Gaussian processes; image motion analysis; image segmentation; image sequences; background subtraction method; background updating problem; image sequences; moving object segmentation; running Gaussian average background model; Background noise; Computer applications; Digital images; Humans; Image sequences; Layout; Noise reduction; Object detection; Object segmentation; Video surveillance; auto-thresholding; background subtraction; background update; background updating time; moving object segmentation; shadow detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Image Computing: Techniques and Applications (DICTA), 2008

Conference_Location

Canberra, ACT

Print_ISBN

978-0-7695-3456-5

Type

conf

DOI

10.1109/DICTA.2008.15

Filename

4699995