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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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1 GAO Li~(1,2),YANG Shu-yuan~2,LIANG Jun-li~(1,2),LI Hai-qiang~3(1.Graduate School of the Chinese Academy of Sciences,Beijing 100039,China;2.Department of Digital System Integration Technique,Institute of Acoustics,CAS,Beijing 100080,China;3.Sonyericsson(China) R&D Center,Beijing 100102,China);Automatic Extraction of Moving Object in Video Sequences[J];Transactions of Beijing Institute of Technology;2006-03
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