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A Framework for Feature Selection for Background Subtraction

10 years 8 months ago
A Framework for Feature Selection for Background Subtraction
Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveillance, human motion analysis, etc. Various statistical approaches have been proposed for modeling a given scene background. However, there is no theoretical framework for choosing which features to use to model different regions of the scene background. In this paper we introduce a novel framework for feature selection for background modeling and subtraction. A boosting algorithm, namely RealBoost, is used to choose the best combination of features at each pixel. Given the probability estimates from a pool of features calculated by Kernel Density Estimate (KDE) over a certain time period, the algorithm selects the most useful ones to discriminate foreground objects from the scene background. The results show that the proposed framework successfully selects appropriate features for different parts of the image....
Toufiq Parag, Ahmed M. Elgammal, Anurag Mittal
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Toufiq Parag, Ahmed M. Elgammal, Anurag Mittal
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