確率モデルと予測モデルを組み合わせたハイブリッド型動的背景モデルによる高速省メモリな物体検出

概要

論文の詳細を見る
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.
社団法人電気学会の論文
2009-05-01