基于自适应背景图像更新的运动目标检测方法

在运动目标的实时检测中常用的方法是背景图像差分法,但因其缺乏背景图像随监视场景光照变化而及时更新的合理方法,限制了本方法的适应性.对此,本文首先提出了一种基于光流场等技术的自适应背景逼近更新方法,并根据彩色差值模型得到差分图像;然后引入Gauss模型实现运动目标的自适应阈值分割.实验结果表明:本文提出的背景更新方法可随着光照条件的变化实时、准确地更新背景图像,在此基础上提出的基于Gauss模型的自适应阈值分割方法可以实现运动目标的完整分割,这为运动目标的后续识别与理解奠定了基础.

A Moving Object Detection Method Based on Self-Adaptive Updating of Background

For real-time detection of moving object,the general and simple method is based on background image difference.However,it requires the accurate current background image,and so far,no reasonable approach has been designed and implemented for automatic background updating along with the illumination variance,which limits its applications.To overcome the above problem,a new self-adaptive background approximating and updating algorithm based on optical flow theory is first presented in this paper.Moreover,the difference image is obtained by using a color image difference model,and then a self-adaptive thresholding segmentation method for moving object detection based on Gauss model is developed and implemented.Experimental results demonstrate that the proposed new background updating method can update the background exactly and quickly along with the variance of illumination,and the self-adaptive thresholding segmentation method based on Gauss model can extract the moving object regions accurately and completely,which is the foundation for further objects recognition and understanding.