A multimodal background model based on binning kernel density estimation
基于分箱核密度估计的非参数多模态背景模型

A novel nonparametric multimodal background model was proposed to detect moving objects.The binned kernel density estimators were exploited to estimate the probability density function of background intensity in training sequence.Based on the gravity center of the data points,the binned kernel density estimators described the key information of the original whole sample set and avoided the repetition computation in the evaluation phase.Compared with algorithm based on the whole samples,the proposed approach is proved to be efficient in traffic surveillance systems,and it can be used in outdoor environment surveillance systems.