一种用于运动目标检测的多模态非参数背景模型

提出了一种基于多样性采样原理的高斯核密度估计模型用于多模态背景描述.从包含运动物体的训练序列中,提取具有较高频度以及最大多样性的样本集用于背景建模.并根据新样本及邻域点在总样本集中取值的相关频度计算权值,避免了采用全部训练点产生的信息冗余和重复计算等缺点,使背景核估计的计算简单有效.对复杂场景下车辆监控系统进行实验,结果表明,该算法在提取运动物体中是有效的.

A Multimodal Nonparametric Background Model for Moving Object Detection

A novel diversity-sampling based Gaussian kernel density estimation (KDE) model was proposed for the representation of multimodal background. Choosing those samples that have diversiform gray-levels in training sequence, a nonparametric model was built for modeling the scene background. According to the related gray-level, the different weights are given to the different samples in kernel density estimation. This avoids the repetition computation using the total samples, and makes KDE very effective. The experimental results show the good detection performance in the traffic surveillance system.