Object Detection Based on Combining Multiple Background Modelings

Abstract

We propose a new method for background modeling based on combination of multiple models. Our method consists of three complementary approaches. The first one, or the pixel-level background modeling, uses the probability density function to approximate background model, where the PDF is estimated non-parametrically by using Parzen density estimation. Then the pixel-level background modeling can adapt periodical changes of pixel values. The region-level background modeling is based on the evaluation of local texture around each pixel, which can reduce the effects of variations in lighting. It can adapt gradual change of pixel value. The frame-level background modeling detects sudden and global changes of the image brightness and estimates a present background image from input image referring to a model background image, and foreground objects can be extracted by background subtraction. In our proposed method, integrating these approaches realizes robust object detection under varying illumination, whose effectiveness is shown in several experiments.