【摘要】：智能交通管理系統(tǒng)是目前21世紀(jì)道路交通管理的發(fā)展趨勢(shì)。高速公路的不斷快速發(fā)展和車輛管理體制的不斷完善,為智能交通管理系統(tǒng)進(jìn)入實(shí)際應(yīng)用領(lǐng)域提供了契機(jī)。基于視頻的智能交通管理系統(tǒng)能檢測(cè)出很多交通參數(shù),其中一個(gè)重要參數(shù)就是車流量。因此基于視頻的交通車流量檢測(cè)技術(shù)成為了智能交通管理系統(tǒng)的重要研究領(lǐng)域。 本文根據(jù)實(shí)際的交通狀況,以車輛交通圖片為研究對(duì)象,重點(diǎn)研究的是實(shí)現(xiàn)一種實(shí)時(shí)道路車輛識(shí)別方法,即如何快速、準(zhǔn)確地在復(fù)雜背景的圖像中識(shí)別出所有目標(biāo)車輛的檢測(cè)。在總結(jié)和分析現(xiàn)有的車輛檢測(cè)的基礎(chǔ)上,針對(duì)視頻圖像的預(yù)處理、車輛的檢測(cè)等問(wèn)題進(jìn)行了探索和研究,提出一種幀差法和Haar-like特征相結(jié)合的車輛檢測(cè)的算法,并通過(guò)實(shí)驗(yàn)證明了新方法的有效性。 本文在現(xiàn)有的研究基礎(chǔ)上,分析了Haar-like矩形特征的形式及利用積分圖像法快速計(jì)算方法。并采用AdaBoost算法訓(xùn)練分類器及采用了12種矩形特征來(lái)表示車輛特征,并對(duì)車底陰影部分用單特征表示,并將車輛的兩個(gè)垂直邊緣與車底陰影部分組成“U”形特征。 本文根據(jù)車輛特征識(shí)別的不同要求,在VC平臺(tái)下調(diào)用OpenCV標(biāo)準(zhǔn)庫(kù)函數(shù)應(yīng)用C語(yǔ)言對(duì)識(shí)別的要求進(jìn)行了編程實(shí)現(xiàn)。并把該算法應(yīng)用到實(shí)時(shí)道路上采集的交通車輛圖像中進(jìn)行了相應(yīng)的實(shí)驗(yàn)測(cè)試：車流量檢測(cè)。并且根據(jù)測(cè)試實(shí)驗(yàn)的內(nèi)容和結(jié)果分別給出在車輛外形多樣性和外界環(huán)境復(fù)雜性下的實(shí)驗(yàn)統(tǒng)計(jì)數(shù)據(jù)和性能評(píng)價(jià)。
[Abstract]:Intelligent traffic management system is the development trend of road traffic management in the 21 st century. The rapid development of expressway and the continuous improvement of vehicle management system provide an opportunity for intelligent traffic management system to enter the field of practical application. Video-based intelligent traffic management system can detect many traffic parameters, one of which is traffic flow. Therefore, video-based traffic flow detection technology has become an important research field of intelligent traffic management system. According to the actual traffic situation, this paper takes the vehicle traffic picture as the research object, and focuses on how to realize a real-time road vehicle recognition method, that is, how to identify all the target vehicles quickly and accurately in the complex background image. On the basis of summing up and analyzing the existing vehicle detection, this paper explores and studies the problems of video image preprocessing and vehicle detection, and proposes a vehicle detection algorithm which combines frame difference method with Haar-like features, and the effectiveness of the new method is proved by experiments. In this paper, on the basis of the existing research, the form of Haar-like rectangular features and the fast calculation method by integral image method are analyzed. The AdaBoost algorithm is used to train the classifier and 12 kinds of rectangular features are used to represent the vehicle features, and the shadow part of the bottom of the vehicle is represented by a single feature, and the two vertical edges of the vehicle and the shadow part of the bottom of the vehicle are formed into "U" features. In this paper, according to the different requirements of vehicle feature recognition, the recognition requirements are programmed with OpenCV standard library function and C language on VC platform. The algorithm is applied to the traffic vehicle image collected on the real-time road to carry out the corresponding experimental test: traffic flow detection. According to the content and results of the test experiment, the experimental statistical data and performance evaluation under the condition of vehicle shape diversity and external environment complexity are given respectively.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.41;U495

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 文學(xué)志;方巍;鄭鈺輝;;一種基于類Haar特征和改進(jìn)AdaBoost分類器的車輛識(shí)別算法[J];電子學(xué)報(bào);2011年05期

2 李良華;羅彬杰;;針對(duì)特定目標(biāo)提取的二值化算法[J];計(jì)算機(jī)科學(xué);2009年12期

3 王哲;常發(fā)亮;;一種基于立體視覺(jué)的運(yùn)動(dòng)目標(biāo)檢測(cè)算法[J];計(jì)算機(jī)應(yīng)用;2006年11期

4 王圣男,郁梅,蔣剛毅;智能交通系統(tǒng)中基于視頻圖像處理的車輛檢測(cè)與跟蹤方法綜述[J];計(jì)算機(jī)應(yīng)用研究;2005年09期

5 邵平;楊路明;;基于模板分解和積分圖像的快速Kirsch邊緣檢測(cè)[J];自動(dòng)化學(xué)報(bào);2007年08期

相關(guān)博士學(xué)位論文 前2條

1 許言午;面向行人檢測(cè)的組合分類計(jì)算模型與應(yīng)用研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2009年

2 唐東明;聚類分析及其應(yīng)用研究[D];電子科技大學(xué);2010年

，

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