A Fast Method for Building and Updating Background Model

Juan Zhu, Yongping Kong
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引用次数: 2

Abstract

On the basic of the video traffic surveillance system, the Vehicle detection is a crucial step. A typical method is background subtraction. Extracting and updating the background plays an important role on speed and efficiency of detection. For this reason, the author puts forward a fast and effective method for building and updating the background model. First, adopt the improved statistical method to build initial background. Second, get the vista of the picture per frame by means of background subtraction, and bring forward the background updating method based on the container technology. Finally, in its updating stage, use the pixel excluding the pixel in vista region to renew the background model. The data of experimental picture states clearly that competed with the traditional method, detection technology is robust and accurate. Furthermore, on the realization of arithmetic, it is easier to operate and it has higher timeliness.
一种快速建立和更新背景模型的方法
在视频交通监控系统的基础上,车辆检测是至关重要的一步。一种典型的方法是背景减法。背景的提取和更新对检测的速度和效率起着至关重要的作用。为此,作者提出了一种快速有效的建立和更新背景模型的方法。首先,采用改进的统计方法建立初始背景。其次,通过背景减法得到每帧图像的远景,并提出了基于容器技术的背景更新方法;最后,在其更新阶段,使用不包括vista区域像素的像素来更新背景模型。实验图像数据表明,与传统方法相比,检测技术具有鲁棒性和准确性。在算法的实现上,更易于操作,具有更高的时效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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