Background model based on statistical local difference pattern

Satoshi Yoshinaga, Atsushi Shimada, Hajime Nagahara, Rin Ichiro Taniguchi

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

15 Citations (Scopus)


We present a robust background model for object detection and report its evaluation results using the database of Background Models Challenge (BMC). Our background model is based on a statistical local feature. In particular, we use an illumination invariant local feature and describe its distribution by using a statistical framework. Thanks to the effectiveness of the local feature and the statistical framework, our method can adapt to both illumination and dynamic background changes. Experimental results, which are done thanks to the database of BMC, show that our method can detect foreground objects robustly against background changes.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2012 International Workshops, Revised Selected Papers
Number of pages6
EditionPART 1
Publication statusPublished - 2013
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: Nov 5 2012Nov 9 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7728 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other11th Asian Conference on Computer Vision, ACCV 2012
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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