發(fā)布時間：2018-10-13 19:55

【摘要】：車輛自動識別技術(shù)是智能交通系統(tǒng)（ITS）的重要組成部分，通過對車輛進行自動識別，可以為交通管理、收費、調(diào)度、統(tǒng)計提供數(shù)據(jù)。車型識別是智能交通領(lǐng)域的研究熱點和難點之一，目前我國的車型識別率還難以滿足使用要求，對車型識別率提高算法的研究勢在必行,本文研究的就是基于車臉圖像特征的車型識別率提高算法。 本文首先對車輛進行檢測與中值濾波，建立車輛樣本庫。然后截取了車臉圖像，并分別采用灰度特征、Canny邊緣特征、Sobel邊緣特征和HOG特征來表示圖像，，通過支持向量機訓練并檢測樣本，獲得各自的識別率，比較并分析其結(jié)果。接著通過一種投票提升算法，將24種識別率不高的Gabor特征組合起來進行車型識別，以提高識別率。 本文使用華碩A43EI235SD-SL筆記本電腦，在Windows7操作系統(tǒng)中，用OpenCV和VS2008搭建實驗平臺。實驗共采集了100類車型，每類車型各1張訓練和測試樣本，車臉圖像尺寸為192*64。實驗中利用灰度特征的識別率為53%，平均識別時間為39.47ms/張；采用Canny邊緣特征，識別率為55%，平均識別時間為47.85ms/張；采用Sobel邊緣特征，識別率為69%，平均識別時間為46.37ms/張；采用HOG特征，識別率為78%，平均識別時間為59.82ms/張；將24種gabor特征識別結(jié)果投票提升，識別率可以達到81%，識別平均時間85.24ms/張。 實驗結(jié)果表明，本文使用的投票提升算法，識別率不僅優(yōu)于單個gabor特征，也優(yōu)于前面幾種特征表示法，代價是增加了一定的時間消耗。
[Abstract]:Automatic vehicle recognition is an important part of Intelligent Transportation system (ITS). It can provide data for traffic management, charge, scheduling and statistics through automatic identification of vehicles. Vehicle recognition is one of the research hotspots and difficulties in the field of intelligent transportation. At present, the vehicle recognition rate in our country is still difficult to meet the requirements of application, so it is imperative to study the algorithm of improving vehicle recognition rate. In this paper, the vehicle recognition rate improvement algorithm based on vehicle face image features is studied. First of all, vehicle detection and median filtering are carried out to establish the vehicle sample bank. Then, the images are captured and represented by gray level feature, Canny edge feature, Sobel edge feature and HOG feature, respectively. The recognition rate is obtained by training and detecting samples by support vector machine (SVM), and the results are compared and analyzed. Then 24 Gabor features with low recognition rate are combined to improve the recognition rate by a voting lifting algorithm. This paper uses Asus A43EI235SD-SL notebook computer, in Windows7 operating system, using OpenCV and VS2008 to build the experimental platform. A total of 100 kinds of vehicle models were collected, with one training and testing sample for each type of vehicle. The image size of the face of the vehicle was 1922 ~ (64). In the experiment, the recognition rate of gray feature is 53 and the average recognition time is 39.47ms/ sheet; using Canny edge feature, the recognition rate is 55 and the average recognition time is 47.85ms/ sheet; using Sobel edge feature, the recognition rate is 69 and the average recognition time is 46.37ms/ sheet; using HOG feature, The recognition rate is 78 and the average recognition time is 59.82ms/, and the result of 24 gabor features can be improved by voting, the recognition rate can reach 81 and the average recognition time is 85.24ms/. The experimental results show that the recognition rate of the proposed voting lifting algorithm is better than that of the single gabor feature and several previous feature representations at the cost of a certain amount of time consumption.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U495;TP391.41

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