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

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)846-852+11
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number5
Publication statusPublished - 2009

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


Dive into the research topics of 'Object detection based on fast and low-memory hybrid background model'. Together they form a unique fingerprint.

Cite this