自适应核密度估计运动检测方法

提出了一种自适应的核密度估计(Kernel density estimation, KDE)运动检测算法. 算法首先提出一种自适应前景、背景阈值的双阈值选择方法, 用于像素分类. 该方法用双阈值克服了单阈值分类存在的不足, 阈值的选择能自适应进行, 且能适应不同的场景. 在此基础上, 本文提出了基于概率的背景更新模型, 按照像素的概率来更新背景, 并利用帧间差分背景模型和KDE分类结果解决背景更新中的死锁问题, 同时检测背景的突然变化. 实验证明了所提出方法的适应性和可靠性.

Adaptive Kernel Density Estimation for Motion Detection

This paper proposed a method of adaptive kernel density estimation (KDE) for motion detection. To begin with, an approach for adaptive selecting thresholds of foreground and background was proposed. By using the two thresholds, the approach can overcome defects of using only one threshold. More importantly, these two thresholds can be selected automatically and they are independent of scenes. Meanwhile, a background model updated according to probability was also provided. The background model of inter-frame difference incorporated with results of KDE can solve deadlock situations in background model. It can also be used to detect suddenly changed background. Experimental results were given to demonstrate that the proposed algorithms are suitable and effective for motion detection.