Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Abstract

Abstract:Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.

Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection

“…This model is able to fit background changes, more or less quickly according to an adjustable learning rate. Bouwmans et al provided a review and an original classification of the numerous improvements of this initial GMM subtraction algorithm [15].…”

Tracking a Moving Objects Using Foreground Detector and Improved Morphological Filter

“…Background subtraction, using GMM, the output contains a lot of noise and the moving object separates, the two fundamental operations of the morphological filtering are erosion and dilation [14]. In this article we will use the aperture filter, which has been successfully applied to the binary image.…”

“…Many related research can be distinguished such as the search by matching [3], search by mean shift [4], search by optical/median flow [5] and bayesian search [6]. Most of the work related to this problem is oriented in terms of the color, size, shape or movement of the mobile, but size plays a very important role in the use of particles.…”

“…The extraction of points in the first image is done using the Harris detector. To estimate a global motion model in the image, many use the RMRm algorithm developed by J-M Obodez [5]. It allows, from a robust multi-scale estimation, to define a 2D motion model with several Parameters between two images.…”

Foreground detection of group-housed pigs based on the combination of Mixture of Gaussians using prediction mechanism and threshold segmentation

“…Stauffer (Stauffer & Grimson, 1999 presented the MoG model to build the background based on a K Gaussian distribution. It can handle multi-modal situations in the background modelling process, and has been widely used and improved in many studies since then (Bouwmans, 2011;Bouwmans, Baf, & Vachon, 2008). In addition, for large and slow foreground objects, a foreground detection method based on a spaceetime combination can detect objects more completely and boundary contours more accurately (Zhou & Zhang, 2006).…”