Object Detection in Nonstationary Scenes Based on Background Modeling
Fuqiao Hu · Jisuanji gongcheng · 2008
Background modeling is an important issue in accurate detection of moving objects.This paper presents a novel non-parametric foreground-background model which explores the complex temporal and spatial dependencies in nonstationary scenes.The model estimates the probability of observing pixels’ five-dimensioned feature vector which represents its intensity values and spatial position information.The model is built and rolling-updated by kernel density estimation.And a Maximum A Posteriori-Markov Random Field(MAP-MRF) decision framework is used to segment the foreground and background by solving a graph-cut.Extensive experiments with nonstationary scenes demonstrate the utility and performance of the proposed approach.