Binary descriptor based nonparametric background modeling for foreground extraction by using detection theory

Min Hsiang Yang, Chun Rong Huang, Wan Chen Liu, Shu Zhe Lin, Kun Ta Chuang

研究成果: Article同行評審

20 引文 斯高帕斯(Scopus)

摘要

Recently, most background modeling approaches represent distributions of background changes by using parametric models such as Gaussian mixture models. Because of significant illumination changes and dynamic moving backgrounds with time, variations of background changes are hard to be modeled by parametric background models. Moreover, how to efficiently and effectively update parameters of parametric models to reflect background changes remains a problem. In this paper, we propose a novel coarse-to-fine detection theory algorithm to extract foreground objects on the basis of nonparametric background and foreground models represented by binary descriptors. We update background and foreground models by a first-in-first-out strategy to maintain the most recent observed background and foreground instances. As shown in the experiments, our method can achieve better foreground extraction results and fewer false alarms of surveillance videos with lighting changes and dynamic backgrounds in both collected and CDnet 2012 benchmark data sets.

原文English
文章編號6915860
頁(從 - 到)595-608
頁數14
期刊IEEE Transactions on Circuits and Systems for Video Technology
25
發行號4
DOIs
出版狀態Published - 2015 四月 1

All Science Journal Classification (ASJC) codes

  • 媒體技術
  • 電氣與電子工程

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