讨论了在适当条件下，密度函数核估计的一致强相合性。
Under certain conditions, We discuss the uniform strong consistency of kernal estimator for the density function.
通过相隔固定的帧差值阅值化得到背景样本值，并采用高斯核密度估计方法计算背景灰度的概率密度函数。
The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity.
本文提出了利用一维核函数构造多维密度函数一个新估计的方法。
In this paper, a new kernel estimator of multivariate density is proposed by using a univariate kernel function.
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