Object detection using local difference patterns

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

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

14 Citations (Scopus)


We propose a new method of background modeling for object detection. Many background models have been previously proposed, and they are divided into two types: "pixel-based models" which model stochastic changes in the value of each pixel and "spatial-based models" which model a local texture around each pixel. Pixel-based models are effective for periodic changes of pixel values, but they cannot deal with sudden illumination changes. On the contrary, spatial-based models are effective for sudden illumination changes, but they cannot deal with periodic change of pixel values, which often vary the textures. To solve these problems, we propose a new probabilistic background model integrating pixel-based and spatial-based models by considering the illumination fluctuation in localized regions. Several experiments show the effectiveness of our approach.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages12
EditionPART 4
Publication statusPublished - 2011
Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: Nov 8 2010Nov 12 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume6495 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th Asian Conference on Computer Vision, ACCV 2010
Country/TerritoryNew Zealand

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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