%0 Journal Article
%T Robust foreground detection with adaptive threshold estimation<br>复杂背景下的自适应前景分割算法
%A Jiang Peng
%A qin xiao lin
%A <br>蒋鹏
%A 秦小麟
%J 中国图象图形学报
%D 2011
%I 
%X A robust background subtraction technique is proposed based on adaptive clustering of temporal color/intensity. An un-supervised clustering method is proposed to model a background with a group of weighted clusters. The clusters and their weights can be updated with a background change. In addition, the unimodal or multimodal distributions of background are detected adaptively. We also present a novel statistical threshold estimation scheme to determine the thresholds using in our method. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.
%K background subtraction
%K kernel density estimation
%K statistical threshold estimation<br>背景差
%K 核密度
%K 阈值估计
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=E83D32C4C0EF63D1B0D04DBF7C8555E0&yid=9377ED8094509821&vid=7801E6FC5AE9020C&iid=CA4FD0336C81A37A&sid=42425781F0B1C26E&eid=BE33CC7147FEFCA4&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=20