A Novel Background Subtraction Method for Moving Vehicle Detection (特集 イノベーションを支える最新の計測技術2012)
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概要
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Background subtraction is a method typically used to segment moving vehicles in image sequences taken from a static camera by comparing each new frame with a model of the background scene. This paper presents a robust background subtraction algorithm which reduces the influence of illumination changes and shadows and adapts to rapid changes in the traffic scene. A statistical background modeling method is presented, which is based on a histogram at the pixel level and produces a color model from a series of frames. For foreground detection, we propose the Choquet integral to fuse the three color-component similarity measures and a texture similarity measure based on a uniform local binary pattern. Finally, we propose a new adaptive background maintenance method. The experimental results for several dataset videos show that the proposed method is more efficient, robust, and accurate than classical approaches.
著者
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Lu Xiaofeng
Graduate School of Science and Technology, Nihon University
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Izumi Takashi
Graduate School of Science and Technology, Nihon University
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Teng Lin
Graduate School of Science and Technology, Nihon University
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Horie Tadahiro
Graduate School of Science and Technology, Nihon University
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Wang Lei
Faculty of Computer Science and Engineering, Xi'an University of Technology
関連論文
- A Novel Background Subtraction Method for Moving Vehicle Detection (特集 イノベーションを支える最新の計測技術2012)
- A Novel Background Subtraction Method for Moving Vehicle Detection (特集 イノベーションを支える最新の計測技術2012)