核密度估计与高斯模型联级运动目标检测

高斯模型与核密度估计模型是两种有效的背景建模及目标检测方法。高斯模型运算简单，但对复杂背景的描述能力差;核密度估计模型对背景描述能力强，但运算复杂，难以实现实时性检测。提出了一种分层联级检测机制，由高斯模型对大部分相对稳定的像素进行分割与检测，对于高斯模型无法精确描述的小部分像素通过核密度估计模型完成分割与检测。实验证实了该方法在适应动态背景扰动与运行效率方面的有效性。

Target detection using kernel density estimation and Gaussian model cascade mechanism

Gaussian model and kernel density estimation model are effective ways for background modeling.Calculation of Gaussian model is simple,however,it suffers from low robustness when there are dynamic scenes and/or sudden lighting changes.Kernel density estimation is robustness but it is too complex to calculate in real-time.A cascade detection mechanism is proposed.Most of the stable pixels are segmented by Gaussian model.After that,for a small part of the pixels that the Gaussian model can not accurately describe are segmented by kernel density estimation model.Experiments confirm that the proposed method is effective to deal with dynamic backgrounds and fast in computation.