Object detection under varying illumination based on adaptive background modeling considering spatial locality

Tatsuya Tanaka, Atsushi Shimada, Daisaku Arita, Rin Ichiro Taniguchi

Research output: Contribution to journalConference articlepeer-review

12 Citations (Scopus)

Abstract

We propose a new method for background modeling. Our method is based on the two complementary approaches. One uses the probability density function(PDF) to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. And foreground object is detected based on the estimated PDF. The other method is based on the evaluation of the local texture at pixel-level resolution while reducing the effects of variations in lighting. Fusing their approach realize robust object detection under varying illumination. Several experiments show the effectiveness of our approach.

Original languageEnglish
Pages (from-to)645-656
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5414 LNCS
DOIs
Publication statusPublished - 2009
Event3rd Pacific Rim Symposium on Image and Video Technology, PSIVT 2009 - Tokyo, Japan
Duration: Jan 13 2009Jan 16 2009

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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