背景モデル低コスト化のためのヒストグラムの指数加重更新法

Translated title of the contribution: Exponentially Weighted Update of Histogram for Background Modeling Reducing Memory Usage

峰松 翼, 五十嵐 正樹, 島田 敬士, 長原 一, 谷口 倫一郎

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a background model by using an exponentially weighted updating method. We realize to reduce memory usage for construction of background model. Our background model is represented as a histogram according to pixel values. Our model uses an exponential increasing weight for updating our model. In our model, recently observed pixels have a bigger influence on the background model than older ones. Therefore, our model gradually ignores the effect of old-observed value on a background model without retaining past pixel values. We apply our method to background subtraction for comparing with conventional methods using kernel density estimation. In experiments, we confirmed that the detection accuracy of our background model is comparable to that of conventional methods.
Translated title of the contributionExponentially Weighted Update of Histogram for Background Modeling Reducing Memory Usage
Original languageJapanese
Pages (from-to)191-200
Number of pages10
JournalJournal of the Institute of Image Electronics Engineers of Japan
Volume45
Issue number2
DOIs
Publication statusPublished - 2016

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