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J. Imaging 2018, 4(7), 92; https://doi.org/10.3390/jimaging4070092

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

1
Laboratoire d’Informatique Signal et Image de la Côte d’Opale, 62228 Calais, France
2
Department of physics and electronics, Lebanese University, 1003 Beirut, Lebanon
*
Author to whom correspondence should be addressed.
Received: 15 May 2018 / Revised: 14 June 2018 / Accepted: 28 June 2018 / Published: 10 July 2018
(This article belongs to the Special Issue Detection of Moving Objects)
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Abstract

Moving foreground detection is a very important step for many applications such as human behavior analysis for visual surveillance, model-based action recognition, road traffic monitoring, etc. Background subtraction is a very popular approach, but it is difficult to apply given that it must overcome many obstacles, such as dynamic background changes, lighting variations, occlusions, and so on. In the presented work, we focus on this problem (foreground/background segmentation), using a type-2 fuzzy modeling to manage the uncertainty of the video process and of the data. The proposed method models the state of each pixel using an imprecise and adjustable Gaussian mixture model, which is exploited by several fuzzy classifiers to ultimately estimate the pixel class for each frame. More precisely, this decision not only takes into account the history of its evolution, but also its spatial neighborhood and its possible displacements in the previous frames. Then we compare the proposed method with other close methods, including methods based on a Gaussian mixture model or on fuzzy sets. This comparison will allow us to assess our method’s performance, and to propose some perspectives to this work. View Full-Text
Keywords: background subtraction; gaussian mixture model; type-2 fuzzy sets; optical flow background subtraction; gaussian mixture model; type-2 fuzzy sets; optical flow
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Darwich, A.; Hébert, P.-A.; Bigand, A.; Mohanna, Y. Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection. J. Imaging 2018, 4, 92.

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