%0 Journal Article
%T A multimodal background model based on binning kernel density estimation<br>基于分箱核密度估计的非参数多模态背景模型
%A WANG Guo-liang
%A LIANG De-qun
%A WANG xin-nian
%A WANG yan-chun
%A <br>王国良
%A 梁德群
%A 王新年
%A 王彦春
%J 计算机应用
%D 2007
%I 
%X A novel nonparametric multimodal background model was proposed to detect moving objects.The binned kernel density estimators were exploited to estimate the probability density function of background intensity in training sequence.Based on the gravity center of the data points,the binned kernel density estimators described the key information of the original whole sample set and avoided the repetition computation in the evaluation phase.Compared with algorithm based on the whole samples,the proposed approach is proved to be efficient in traffic surveillance systems,and it can be used in outdoor environment surveillance systems.
%K nonparametric background model
%K moving object detection
%K kernel density estimation
%K binning rule<br>非参数背景模型
%K 运动目标检测
%K 核密度估计
%K 分箱规则
%K 分箱规则
%K 密度估计
%K 非参数
%K 多模态
%K 背景模型
%K kernel
%K density
%K estimation
%K based
%K 交通监控系统
%K 户外
%K 有效性
%K 运动目标检测
%K 发现
%K 检测算法
%K 全样本
%K 实时性
%K 重复计算
%K 样本数据
%K 关键信息
%K 提取
%K 数据重心
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD27430512D8BF62D1084AE6BD&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=94C357A881DFC066&sid=F88EF6DD822B0FF5&eid=7D34DED3F877BD2D&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=12