Title :
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
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;
Conference_Titel :
Digital Image Computing: Techniques and Applications (DICTA), 2008
Conference_Location :
Canberra, ACT
Print_ISBN :
978-0-7695-3456-5
DOI :
10.1109/DICTA.2008.15
