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Robust moving object detection under complex background
Author(s) -
Ying Ding,
Wenhui Li,
Fan Jing-tao,
Huamin Yang
Publication year - 2010
Publication title -
computer science and information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 24
eISSN - 2406-1018
pISSN - 1820-0214
DOI - 10.2298/csis1001201d
Subject(s) - background subtraction , computer science , artificial intelligence , robustness (evolution) , computer vision , object detection , measure (data warehouse) , object (grammar) , foreground detection , fuzzy logic , pattern recognition (psychology) , pixel , data mining , biochemistry , chemistry , gene
We present a novel method to robustly and efficiently detect moving object, even under the complexity background, such as illumination changes, long shadows etc. This work is distinguished by three key contributions. The first is the integration of the Local Binary Pattern texture measure which extends the moving object detection work for light illumination changing. The second is the introduction of HSI color space measure which removes shadows for the background subtraction. The third contribution is a novel fuzzy way using the Choquet integral which improves detection accuracy. The experiment results using several dataset videos show the robustness and effectiveness of the proposed method.

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