Open AccessAutomatic foreground detection based on KDE and binary classification
Author(s) -
Mohammed Lahraichi,
Khalid Housni,
Samir Mbarki
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v15.i1.pp517-526
Subject(s) - markov random field , pixel , background subtraction , thresholding , artificial intelligence , cut , kernel density estimation , computer science , pattern recognition (psychology) , maximum a posteriori estimation , kernel (algebra) , foreground detection , binary number , markov chain , probability density function , image (mathematics) , graph , computer vision , mathematics , image segmentation , machine learning , maximum likelihood , statistics , arithmetic , theoretical computer science , combinatorics , estimator
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.