基于图像分块的背景模型构建方法

Approach of Constructing Background Model Based on Image Blocks

提出了一种基于图像分块的背景模型构建方法,目的是为了减少像素形式的背景模型所带来的计算冗余,提高系统的运行速度.文中回顾了目前主要的背景提取方法,给出了图像分块的方式以及几种常用的图像块特征,并且利用图像块的特征来构建自适应的高斯混合模型.通过一组视频将该方法与传统的像素形式的背景模型进行了实验对比;结果表明,该方法在保持相同的目标检测率的情况下,大大提高了系统的运行效率.

Based on image blocks, a method for constructing background models is presented to reduce computation redundancy arising from pixel-background model and to improve execution speed of the system. After reviewing the main methods of background extraction up to now, we present a partitioning method and some common features for the image blocks, and construct some adaptive mixture Gaussian models with these features. Experimental comparison between this method and the traditional pixel-background models is made with a group of videos. The results show that this method enhances system execution efficiency greatly at the same finding-out rates.