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

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6915860
Pages (from-to)595-608
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume25
Issue number4
DOIs
Publication statusPublished - 2015 Apr 1

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering

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