基于相对欧氏距离的背景差值法视频目标检测

Video Object Detection of Background Subtraction Method Based on Relative Euclidean Distance

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。

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.