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Approche pixel de la soustraction d'arrière-plan en vidéo, basée sur un mélange de gaussiennes imprécises

Abstract : Moving objects detection is a very important step for many applications such as human behavior analysis surveillance, model-based action recognition, road traffic monitoring, etc. Background subtraction is a popular approach, but difficult given that it must overcome many obstacles, such as dynamic background changes, brightness variations, occlusions, and so on. In the presented works, we focused on this problem of objects/background segmentation, using a type-2 fuzzy modeling to manage the inaccuracy of the model and the data. The proposed method models the state of each pixel using an imprecise and scalable Gaussian mixture model, which is exploited by several fuzzy classifiers to ultimately estimate the pixel class at each image. More precisely, this decision takes into account the history of its evolution, but also its spatial neighborhood and its possible displacements in the preceding images. Then we compared the proposed method with other close methods, including methods based on a gaussian mixture model, fuzzy based methods, or ACP type methods. This comparison allowed us to assess its good performances, and to propose some perspectives to this work.
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Submitted on : Thursday, January 31, 2019 - 2:05:45 PM
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Ali Darwich. Approche pixel de la soustraction d'arrière-plan en vidéo, basée sur un mélange de gaussiennes imprécises. Traitement du signal et de l'image [eess.SP]. Université du Littoral Côte d'Opale; École Doctorale des Sciences et de Technologie (Beyrouth), 2018. Français. ⟨NNT : 2018DUNK0479⟩. ⟨tel-02001886⟩

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