Automatic foreground detection based on KDE and binary classification

Abstract

In the recent decades, several methods have been developed to extract moving objects in the presence of dynamic background. However, most of them use a global threshold, and ignore the correlation between neighboring pixels. To address these issues, this paper presents a new approach to generate a probability image based on Kernel Density Estimation (KDE) method, and then apply the Maximum A Posteriori in the Markov Random Field (MAP-MRF) based on probability image, so as to generate an energy function, this function will be minimized by the binary graph cut algorithm to detect the moving pixels instead of applying a thresholding step. The proposed method was tested on various video sequences, and the obtained results showed its effectiveness in presence of a dynamic scene, compared to other background subtraction models.