[1]蒋翠清,孙富亮,吴艿芯. 基于相对欧氏距离的背景差值法视频目标检测[J].计算机技术与发展,2015,25(01):37-40.
　JIANG Cui-qing,SUN Fu-liang,WU Nai-xin. Video Object Detection of Background Subtraction Method Based on Relative Euclidean Distance[J].,2015,25(01):37-40.
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 基于相对欧氏距离的背景差值法视频目标检测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年01期
页码:
37-40
栏目:
智能、算法、系统工程
出版日期:
2015-01-10

文章信息/Info

Title:
 Video Object Detection of Background Subtraction Method Based on Relative Euclidean Distance
文章编号:
1673-629X（2015)01-0037-04
作者:
 蒋翠清; 孙富亮; 吴艿芯
 合肥工业大学 管理学院
Author(s):
 JIANG Cui-qing; SUN Fu-liang; WU Nai-xin
关键词:
 ; 目标检测; 背景差法; 图像处理; 欧氏距离; 背景建模
Keywords:
 ; object detection; background subtraction; image processing; Euclidean distance; background modeling
分类号:
TP391.41
文献标志码:
A
摘要:
 为了从包含大量冗余信息的监控视频中快速查找到运动目标，提出了一种改进的背景差值目标检测算法。首先，通过灰度化和中值滤波对视频图像进行预处理；其次，对视频帧进行抽样统计，计算各个对应像素点的灰度值的中值，建立背景模型；再次，通过大量的实验确定合适的阈值后，计算当前帧与背景模型之间欧氏距离的相对差值，并由此判断前景帧和背景帧；最后，将含有运动目标的图像或视频截取出来。实验结果表明，该方法可以更加准确有效地检测目标，可用于视频监控(如生活小区、铁路交通、仓库的监控视频等)中的目标检测。
Abstract:
 In order to find the moving object from the surveillance video which contains a lot of redundant information quickly,an im-proved object detection algorithm based on background subtraction is put forward. Firstly,video images are preprocessed by graying and median filtering. Secondly,by statistical sampling of video frames,the median value of gradation value of each corresponding pixel point is calculated,and then a background model can be established. Thirdly,this algorithm can determine the foreground frame and the back-ground frame through the difference of the relative Euclidean distance between the current frame and the background model,but before that,an appropriate threshold must be determined by a large number of experiments. Finally,the images or videos containing moving ob-jects will be clipped out. Experimental results show that this method can detect objects more effectively and accurately,and can be used for object detection ( such as residential,rail transport,warehouse surveillance video,etc) in the video surveillance.

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更新日期/Last Update: 2015-04-17