Object segmentation under varying illumination: stochastic background model considering spatial locality
Tatsuya Tanaka, Atsushi Shimada, Daisaku Arita, Rin-ichiro Taniguchi · Progress in Informatics · 2010
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.