Statistical local difference pattern for background modeling

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

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Object detection is an important task for computer vision applications. Many researchers have proposed a number of methods to detect the objects through background modeling. To adapt to "illumination changes" in the background, local feature-based background models are proposed. They assume that local features are not affected by background changes. However, "motion changes", such as the movement of trees, affect the local features in the background significantly. Therefore, it is difficult for local feature-based models to handle motion changes in the background. To solve this problem, we propose a new background model in this paper by applying a statistical framework to a local feature-based approach. Our proposed method combines the concepts of statistical and local feature-based approaches into a single framework. In particular, we use illumination invariant local features and describe their distribution by Gaussian Mixture Models (GMMs). The local feature has the ability to tolerate the effects of "illumination changes", and the GMM can learn the variety of "motion changes". As a result, this method can handle both background changes. Some experimental results show that the proposed method can detect the foreground objects robustly against both illumination changes and motion changes in the background.

Original languageEnglish
Pages (from-to)198-210
Number of pages13
JournalIPSJ Transactions on Computer Vision and Applications
Volume3
DOIs
Publication statusPublished - Dec 1 2011

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Computer vision

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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Statistical local difference pattern for background modeling. / Yoshinaga, Satoshi; Shimada, Atsushi; Nagahara, Hajime; Taniguchi, Rin Ichiro.

In: IPSJ Transactions on Computer Vision and Applications, Vol. 3, 01.12.2011, p. 198-210.

Research output: Contribution to journalArticle

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