摘要
在背景图像差分的基础上,提出一种改进的实时背景更新算法。通过背景学习过程,利用若干帧连续图像中每点像素灰度值样本,估算对应像素点的灰度值均方差。利用正态分布的“3σ原则”对图像背景进行实时更新,消除背景变化对人体分割的影响。提出基于图像平均灰度值的阈值分割算法,以提高算法对不同光照强度的适应能力。提出基于垂直投影图的阴影消除算法,分析表明,人体躯干和影子区的垂直投影特性不同,由此可以有效消除人体影子。实验结果表明,本算法在不同的光照情况下能够完整分割运动人体图像。
Based on difference background images, an improved real-time background update algorithm was proposed. With the background learning process, the mean square error of grey value of each pixel was estimated by sequence images. According to the 3σ rule of normal distribution, the influence caused by the variety of background was avoided. A threshold segmentation algorithm based on the average grey value of the image was applied, which made the algorithm adapt to different illumination. A shadow removing algorithm based on the vertical projection was proposed. It was shown that there we,re different vertical projection characteristics between human body and shadow. It was used to eliminate the shadow of the motion human efficiently. The experimental results demonstrate that the algorithm can extract the motion human effectively and completely under different cases of illumination.
出处
《光电工程》
EI
CAS
CSCD
北大核心
2007年第6期107-111,共5页
Opto-Electronic Engineering
关键词
图像处理
运动检测
背景更新
图像分割
阴影消除
Image processing
Motion detection
Background update
Image segmentation
Shadow removing
