基于背景差分法的物体识别 OBJECT RECOGNITION BY BACKGROUND DIFFERENCE
METHOD
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摘 要
图像识别技术在很多领域都是产业现代化的基础，所以在当今社会它成为了研究的热门学科。由于被测物体会由于时间的不同，背景的不同，气候的不同,被测物体会表现出不同的外形特征，具有错综复杂和瞬间转变的性质［1］，所以图像识别的准确率一直是一个难以解决的课题。而基于背景差分的图像识别技术则能很好地解决这一困难的问题，它是整个图像识别领域中最基础并且可靠性最高的一门科学［2］。这种方法以将背景图片与实时图像作差分的方法有效提高普通图像识别方法的准确度
［3］
，也可以对运动物体做出准确性较高的识别，并且具有较
快的处理速度，能在OpenCV1.0的帮助下完成目标。目前，基于背景差分的图像识别主要应用于车辆流量检测技术［4］、车牌检测［5］、弱信号的目标检测、稻米粒优劣的判断、运动人体检测等领域。本文使用USB外接摄像头从外界获得背景与被测视频流，在Windows系统下利用Visual Studio C++6.0与OpenCV 1.0结合编程的方法［6］，对背景与被测视频流做差分，从而达到物体识别的目的。由于使用的摄像头分辨率较低，所以测量的结果精确度并不是很高，测量物体也较为简单，如何利用高分辨率的摄像头以及高版本OpenCV函数来识别更复杂的被测物体是今后的研究目标。
关键词： 背景差分； 图像识别； Opencv；Visual Studio C++6.0
ABSTRACT
Image Recognition has been one of the most popular deciplines because that it’s the key of Industrial modernization in numerous fields. The measured objects will be in difference shape for the difference of seasons; background and temperature.They are complex and variable, thus, it’s really a tough work to increase the accuracy of Image Recognition. Image recognition technology based on background subtraction, a subject that is very reliable and accurate in the Image Recognition field, solves that problem well.This method improves the accuracy effectively by differencing the video stream to a background picture, it also can identificat the moving subject effectively, it can progress a subject in high speed by the using of the opencv 1.0. At present, Image Recognition is often utilized in the field such as vehicle traffic detection technology，target detection of weak signals, judge the merits of rice and Human Movement detection,there we get backgroud and video stream from the outside world by USB external camera, programme with opencv 1.0 by visual studio C++6.0 in the operating system of Windows.Then we identificate a subject by differencing backgroud and video stream.The acurracy of Image Recognition is below the ideal situation because of the resolution of USB external camera.Also the subject is very simple.It’s our future research goals that using USB external camera with a high resolution and a high version of the opencv function to identificate more complex objects.
Key words: Background difference；Image Recognition；opencv；visual studio
C++6.0
目 录
摘 要 ......................................................................................................... I ABSTRACT .............................................................................................. II 目 录 ................................................................................................... III 第一章
前言........................................................................................... 1
第一节 计算机领域的物体识别的发展历史 ........................................ 1 第二节 计算机图像获取技术现状及前途 .............................................. 2 第三节 计算机图像识别技术现状及前途 .............................................. 4 第四节 色彩标准分类 .............................................................................. 5 第二章
差分法应用 .............................................................................. 6
第一节 数学中的差分法 .......................................................................... 6 第二节 背景差分法和帧间差分法 .......................................................... 6 第三章
Opencv的介绍与使用 ............................................................. 8
第一节 opencv简介 .................................................................................. 8 第二节 有关图像识别的opencv函数 ..................................................... 9 第四章
利用C++实现图像获取与识别 ............................................ 13
第一节 C++的发展阶段 ......................................................................... 13 第二节C++的特点 .................................................................................. 14 第三节 基于背景差分法的物体识别的程序 ........................................ 14 第四节 程序运行结果 ............................................................................ 22 第五章
结论......................................................................................... 27
参考文献 ................................................................................................... 28 致 谢 ....................................................................................................... 30