计算机工程与应用2011，Vol.47Issue(18)：1-3,3.DOI:10.3778/j.issn.1002-8331.2011.18.001
核密度估计与高斯模型联级运动目标检测
Target detection using kernel density estimation and Gaussian model cascade mechanism
摘要
Abstract
Gaussian model and kernel density estimation model are effective ways for background modeling.Calculation of Gaussian model is simple,however, it suffers from low robustness when there are dynamic scenes and/or sudden lighting changes.Kernel density estimation is robustness but it is too complex to calculate in real-time.A cascade detection mechanism is proposed.Most of the stable pixels are segmented by Gaussian model.After that, for a small part of the pixels that the Gaussian model can not accurately describe are segmented by kernel density estimation model.Experiments confirm that the proposed method is effective to deal with dynamic backgrounds and fast in computation.关键词
目标检测/核密度估计/高斯模型/联级机制Key words
target detection/kernel density estimation/Gaussian model/cascade mechanism分类
信息技术与安全科学引用本文复制引用
芮挺,周遊 ,马光彦,廖明..核密度估计与高斯模型联级运动目标检测[J].计算机工程与应用,2011,47(18):1-3,3.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.61070104,61070108) （the National Natural Science Foundation of China under Grant No.61070104,61070108）
解放军理工大学工程兵工程学院基金. （）
