Probabilistic Model-based Background Subtraction

被引：0
|
作者：
Krüger, V
Anderson, J
Prehn, T
机构：
[1] Aalborg Univ, Aalborg Media Lab, DK-2750 Ballerup, Denmark
[2] Aalborg Univ, DK-6700 Esbjerg, Denmark
来源：
IMAGE ANALYSIS AND PROCESSING - ICIAP 2005, PROCEEDINGS | 2005年 / 3617卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we introduce a model-based background subtraction approach where first silhouettes, which model the correlations between neightboring pixels are being learned and where then Bayesian propagation over time is used to select the proper silhouette model and tracking parameters. Bayes propagation is attractive in our application as it allows to deal with uncertainties in the video data during tracking. We eploy a particle filter for density estimation. We have extensively tested our approach on suitable outdoor video data.
引用
收藏
页码：180 / 187
页数：8
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