توضیحات
ABSTRACT
Background modeling is a key step of background subtraction methods used in the context of static camera. The goal is to obtain a clean background and then detect moving objects by comparing it with the current frame. Mixture of Gaussians Model [1] is the most popular technique and presents some limitations when dynamic changes occur in the scene like camera jitter, illumination changes and movement in the background. Furthermore, the MGM is initialized using a training sequence which may be noisy and/or insufficient to model correctly the background.
INTRODUCTION
The common approach for discriminating moving objects from the background is the background subtraction which is used in the field of video surveillance [2],optical motion capture [3–5] and multimedia applications[6]. In this context,background modeling is the first key step to obtain a clean background.The simplest way to model the background is to acquire a background image which doesn’t include any moving object.In some environments,the background isn’t available and can always be changed undercritical situations like camera jitter,illumination changes, objects being introduced or removed from the scene.
Year: 2008
Publisher : Conference: International Symposium on Visual Computing
By: Fida El Baf, Thierry Bouwmans, Bertrand Vachon
File information: English Language / 11 Page / Size :443 KB
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سال:2008
ناشر: Conference: International Symposium on Visual Computing
کاری از: Fida El Baf, Thierry Bouwmans, Bertrand Vachon
اطلاعات فایل: زبان انگلیسی/ 11 صفحه/ حجم 443 کیلوبایت
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