|
Online ISSN:1349-8606 |
Progress in Informatics |
|
No.7 March 2010 |
|
Page 21-31 |
PDF(1,770KB) | References |
doi:10.2201/NiiPi.2010.7.4 |
Object segmentation under varying illumination: stochastic background model considering spatial locality |
Tatsuya TANAKA1, Atsushi SHIMADA2, Daisaku ARITA3, and Rin-ichiro TANIGUCHI4 |
1,2,4Kyushu University
3Institute of Systems, Information Technologies and Nanotechnologies
|
(Received: October 4, 2009) (Revised: December 16, 2009) (Accepted: December 24, 2009)
|
Abstract:
We propose a new method for background modeling. Our method is based on the two complementary approaches. One uses the probability density function (PDF) to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. Then, foreground object is detected based on the estimated PDF. The method is based on the evaluation of the local texture at pixel-level resolution which reduces the effects of variations in lighting. Fusing those approachs realizes robust object detection under varying illumination. Several experiments show the effectiveness of our approach.
|
Keywords:
Object detection, adaptive background model, illumination change, parzen density estimation, radial reach filter
|
|
PDF(1,770KB) | References |
National Institute of Informatics is a member of CrossRef.
|