Adaptive Background Subtraction using Fuzzy based Gaussian Mixture Model

Authors(1) :-Gulreen Kour

Background Subtraction is one of the initial steps in object tracking in visual surveillance which we all know holds a great importance in today’s world. BGS involves the segmentation of foreground objects by differencing the current frame from the background image or the reference image, but it is not always as simple as that. BGS in practice involves environmental challenges like camera jitter, shadows, camouflage, illumination changes, occlusion, night videos, changing weather etc. A lot of work has been done over the years for coming up with techniques which are robust to these challenges. Here, in this paper we try to study various background subtraction techniques. A study on Gaussian Mixture Model (GMM) has been made and its drawback of uncertain and noisy data are studied and an approach is proposed for overcoming this drawback.

Authors and Affiliations

Gulreen Kour
Department of CSE, SMVDU, Katra, Jammu and Kashmir, India

BGS,T2-FGMM,GMM,Covariance,intervalued T-2 FS

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Publication Details

Published in : Volume 4 | Issue 1 | March-April 2018
Date of Publication : 2018-04-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 28-33
Manuscript Number : CSEIT411805
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Gulreen Kour, "Adaptive Background Subtraction using Fuzzy based Gaussian Mixture Model", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 1, pp.28-33, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT411805

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