二维时空熵运动检测中的自适应门限技术

Adaptive threshold technique for motion detection based on the two-dimensional spatio-temporal entropic principle

针对图像序列中运动检测门限的选取问题,提出一种基于二维时空熵准则自适应确定检测门限的新方法.将运动检测归结为两个二值划分问题,无需已知背景分布的具体形式和参数,利用二维熵准则自适应确定门限矢量(S,T).并提出一种二维熵门限快速计算方法,利用积分和迭代操作避免二维熵门限求解过程中点的重复计算,将灰度级为N的图像的运算量从O(N4)降低到O(N2).实验结果表明,该方法在目标和背景对比度较低的情况下也可提取出完整的运动目标信息,可实现实时处理.

In order to determine the detection threshold in moving objects extraction, an adaptive detection threshold determination method based on the two-dimensional spatio-temporal entropic principle is proposed. The motion detection is reduced to two binary partition problems, and two-dimensional entropic principle is used to determine the threshold vector（S, T）, where the explicit function form and parameters of background distribution are not needed to be known. A fast algorithm for calculating the two-dimensional entropic threshold is put forward, in which repeated calculation is avoided by using the integral and iterative operation, thus reducing the computational complexity from O（N^4 ） to O（N^2） for an N gray levels image. The experimental results show that the information of moving objects which have a low contrast to background can also be extracted completely by this method in real time.