双阈值灰度归类背景重构算法

Two Thresholds Pixel Intensity Classification for the Background Reconstruction

针对背景差法背景重构的难点,提出了一种改进的像素灰度归类的背景重构算法。该方法假定"背景在图像序列中总是最常被观测到",根据帧间灰度差和累计帧差和划分灰度类,对划分的灰度区间执行合并操作,最后选择出现频率最大的灰度类作为该像素的背景值。仿真结果表明,该算法有效地避免了混合现象,当场景本身存在缓慢变化时也能很好地构建出背景,从而有利于后续的运动目标检测、识别和跟踪。

The background subtraction is an important method to detect the moving objects, the difficulty in which is the background reconstruction. Therefore an improved background reconstruction algorithm based on pixel intensity classification is proposed. According to the hypothesis that the background pixel intensity always appears in an image sequence with maximum probability, the adjacent frames will be classified as the same or different classes of intensity based on the frame difference and frame difference accumulation, and then merging procedure is run to classify the classes, finally intensity classes with maximum appearance probability are selected as the background pixel intensity values, Simulations results show that the algorithm could affectively avoid moving mixture and well reconstruct the background of vary scene. It also has quick speed, lower store space, and strong robust.