Background subtraction using Gaussian–Bernoulli restricted Boltzmann machine. Issue 9 (1st September 2018)

• Record Type: Journal Article
• Authors: Sheri, Ahmad Muqeem; Rafique, Muhammad Aasim; Jeon, Moongu; Pedrycz, Witold
• Is Part Of: IET image processing. Volume 12:Issue 9(2018)
• Page Start: 1646
• Page End: 1654
• Publication Date: 2018-09-01
• Subjects: Boltzmann machines -- computer vision -- image sequences -- image segmentation -- image resolution -- Gaussian distribution; Gaussian‐Bernoulli restricted Boltzmann machine -- computer vision -- video sequences -- GRBM -- background subtraction problem -- generative model paradigm -- pixel values -- image reconstuction; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.1055
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The background subtraction is an important technique in computer vision which segments moving objects into video sequences by comparing each new frame with a learned background model. In this work, the authors propose a novel background subtraction method based on Gaussian–Bernoulli restricted Boltzmann machines (GRBMs). The GRBM is different from the ordinary restricted Boltzmann machine (RBM) by using real numbers as inputs, resulting in a constrained mixture of Gaussians, which is one of the most widely used techniques to solve the background subtraction problem. The GRBM makes it easy to learn the variance of pixel values and takes the advantage of the generative model paradigm of the RBM. They present a simple technique to reconstruct the learned background model from a given input frame and to extract the foreground from the background using the variance learned for each pixel. Furthermore, they demonstrate the effectiveness of the proposed technique with extensive experimentation and quantitative evaluation on several commonly used public data sets for background subtraction.