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

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

研究成果: ジャーナルへの寄稿Conference article

12 引用 (Scopus)

抄録

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.

元の言語英語
ページ(範囲)645-656
ページ数12
ジャーナルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5414 LNCS
DOI
出版物ステータス出版済み - 2 19 2009
イベント3rd Pacific Rim Symposium on Image and Video Technology, PSIVT 2009 - Tokyo, 日本
継続期間: 1 13 20091 16 2009

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Background Modeling
Object Detection
Locality
Probability density function
Illumination
Lighting
Density Estimation
Texture
Textures
Pixel
Pixels
Evaluation
Experiment
Object detection
Experiments
Model

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

これを引用

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AU - Shimada, Atsushi

AU - Arita, Daisaku

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JO - Lecture Notes in Computer Science

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