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《Journal of Shanghai Jiaotong University》 2005-S1
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A Multimodal Nonparametric Background Model for Moving Object Detection

MAO Yan-fen, SHI Peng-fei(Inst. of Image Processing & Pattern Recognition, Shanghai Jiaotong Univ., Shanghai 200030, China)  
A novel diversity-sampling based Gaussian kernel density estimation (KDE) model was proposed for the representation of multimodal background. Choosing those samples that have diversiform gray-levels in training sequence, a nonparametric model was built for modeling the scene background. According to the related gray-level, the different weights are given to the different samples in kernel density estimation. This avoids the repetition computation using the total samples, and makes KDE very effective. The experimental results show the good detection performance in the traffic surveillance system.
【Fund】: 国家重点基础研究发展规划(973)项目(TG1998030408)
【CateGory Index】: TP274.4
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【Secondary References】
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1 SUN Lingling JING Gu ZHANH Ke(Harbin University of Science and Technology,college of computer science and technology,Heilongjiang harbin 150080 china);Video Speed Measuring Technology Research and Simulation[J];Science & Technology Information;2009-01
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1 ZOU Yue-xian1 +, WANG Yi-yan1, GUAN PEI2, YANG HUA2, CHEN Wei-rong2 1 Peking University Shenzhen Graduate School, Key Laboratory of Integrated Microsystems, Guangdong Shenzhen 518055, China; 2 Southwest Jiaotong University, National Power Traction Laboratory, Sichuan Chengdu 610031, China;The study of video-based vehicle detection technology in the complex scenes[A];[C];2008
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