一种核密度估计动态场景建模算法

利用非参数估计理论核密度估计方法，研究了多模态场景参考帧的动态维护与更新问题。针对背景存在小运动的复杂场景，提出了一种基于多样性采样机制的核估计动态场景建模算法。从包含运动物体的训练图像序列中，提取具有较高频度和多样性的灰度新样本集，建立背景高斯核密度估计模型，并利用背景帧差值法检测运动物体。多样性样本集很好地表征了训练样本的关键信息，避免了采用全样本产生的信息冗余和重复计算，使估计阶段的计算简单有效。通过与全样本方法在运动物体检测效果上的时比，验证了本文算法的有效性。

Kernel Density Estimation for Dynamic Scene Modeling

A kernel density estimation (KDE) based on a multimodal model is presented for dynamic scene reference frame maintenance and update problems. A diversity sampling schem is proposed to choose a new sample set from the image sequence including moving objects. Using more popular and diversiform intensity samples, a Gaussian KDE is built to estimate the background model and to detect moving objects by background subtraction. The diversity sampling samples describe the key information of the original whole sample set and avoid the repetition computation in the evaluation phase. Compared with the whole samples based on algorithm, the proposed approach is proved to be efficientive in traffic surveillance systems.