基于分箱核密度估计的非参数多模态背景模型

提出了一种用于检测运动目标的非参数多模态背景模型。该模型采用分箱核密度估计算法从训练图像序列中得到背景的密度函数。分箱核密度估计算法利用基于网格数据重心的分箱规则，很好地提取了训练图像序列的关键信息,避免了采用全样本数据点的重复计算, 大大提高了运动目标检测算法的实时性。通过与全样本算法进行对比，发现该背景模型在运动目标检测中的有效性，可用于户外的实时交通监控系统。

A multimodal background model based on binning kernel density estimation

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.