Object detection based on fast and low-memory hybrid background model

研究成果: Contribution to journalArticle査読

4 被引用数 (Scopus)

抄録

We propose a new method to create adaptive background models. Traditionally, each pixel has an adaptive background model which consists of Gaussian mixtures. Each model can approximate small changes and periodic changes of pixel values and it helps us to detect moving objects. However, it cannot adapt to some illumination changes such as gradually varying illumination, precipitously varying illumination and so on. The other model such as using a texture or using prediction of pixel value is effective to handle these changes. Therefore, a hybrid background model which is combined with more than two kind of models. In our approach, we use two different types of the background model. The one is the stochastic background model. The other is the predictive background model based on the exponential smoothing.

本文言語英語
ジャーナルIEEJ Transactions on Electronics, Information and Systems
129
5
DOI
出版ステータス出版済み - 1 1 2009

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

フィンガープリント 「Object detection based on fast and low-memory hybrid background model」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル