Adaptive background modeling for paused object regions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Background modeling has been widely researched to detect moving objects from image sequences. Most approaches have a falsenegative problem caused by a stopped object. When a moving object stops in an observing scene, it will be gradually trained as background since the observed pixel value is directly used for updating the background model. In this paper, we propose 1) a method to inhibit background training, and 2) a method to update an original background region occluded by stopped object. We have used probabilistic approach and predictive approach of background model to solve these problems. The great contribution of this paper is that we can keep paused objects from being trained.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers
Number of pages11
Publication statusPublished - 2011
EventInternational Workshops on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: Nov 8 2010Nov 9 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6468 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Workshops on Computer Vision, ACCV 2010
Country/TerritoryNew Zealand

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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