本文選題：車牌識(shí)別 + Adaboost算法��； 參考：《浙江大學(xué)》2014年碩士論文

【摘要】：車牌識(shí)別是現(xiàn)代智能交通系統(tǒng)的重要組成部分,本文在本課題組研制的車載云臺(tái)攝像機(jī)硬件的基礎(chǔ)上添加車牌識(shí)別功能組成可移動(dòng)式車牌識(shí)別系統(tǒng),相較于傳統(tǒng)的固定式車牌識(shí)別系統(tǒng),應(yīng)用更加靈活,是對(duì)目前廣泛采用的固定式車牌識(shí)別系統(tǒng)的補(bǔ)充。 本文主要針對(duì)車載云臺(tái)攝像機(jī)可移動(dòng)式的特點(diǎn),從運(yùn)動(dòng)車輛檢測(cè)、車牌定位、車牌傾斜校正及字符分割、車牌字符識(shí)別等方面展開研究,其具體工作如下： (1)介紹了車牌識(shí)別的硬件系統(tǒng)。主要分析了車載云臺(tái)攝像機(jī)的基本功能以及搭建本系統(tǒng)的其他硬件設(shè)備,為實(shí)現(xiàn)該系統(tǒng)的軟件算法部分奠定基礎(chǔ)。 (2)研究分析了常用的運(yùn)動(dòng)目標(biāo)檢測(cè)方法。確定了本系統(tǒng)兩種工作模式,即攝像機(jī)靜止和攝像機(jī)運(yùn)動(dòng)。對(duì)于常用的工作模式即攝像機(jī)靜止時(shí),提出了虛擬線框幀差法檢測(cè)運(yùn)動(dòng)車輛,實(shí)驗(yàn)證明該方法簡(jiǎn)單有效。 (3)研究分析了常用的車牌定位方法。提出了基于改進(jìn)haar-like特征和Adaboost分類器的車牌定位方法。針對(duì)車牌區(qū)域的特點(diǎn),設(shè)計(jì)了適合車牌的haar-like特征,通過大量的正負(fù)樣本訓(xùn)練了Adaboost分類器,檢測(cè)時(shí)運(yùn)用積分圖加速檢測(cè)窗口的特征提取。 (4)研究分析了常用的車牌傾斜校正和字符分割技術(shù)。根據(jù)車牌字符具有豐富的垂直邊緣而車牌周圍很少有垂直邊緣的特點(diǎn),提出了基于垂直邊緣特征主元分析的車牌傾斜校正方法,字符分割則采用基于車牌特征和連通域分析的車牌字符分割方法。 (5)研究分析了常用的車牌字符識(shí)別技術(shù)。本文首先提取出字符的Gabor特征和粗網(wǎng)格特征,再運(yùn)用BP神經(jīng)網(wǎng)絡(luò)的方法,訓(xùn)練出車牌字符識(shí)別的分類器。 (6)根據(jù)實(shí)驗(yàn)結(jié)果總結(jié)分析了該系統(tǒng)有待改進(jìn)和提高的地方,確定接下來的研究工作。
[Abstract]:License plate recognition is an important part of modern intelligent transportation system. In this paper, the mobile license plate recognition system is composed by adding the license plate recognition function to the hardware of the vehicle-mounted cloud head camera developed by our research group. Compared with the traditional fixed license plate recognition system, the application is more flexible, and it is a supplement to the widely used fixed license plate recognition system. This paper mainly aims at the mobile characteristics of the vehicle head camera, from the moving vehicle detection, license plate location, license plate tilt correction and character segmentation, license plate character recognition and other aspects of research, its specific work is as follows: The hardware system of license plate recognition is introduced. This paper mainly analyzes the basic functions of the vehicle-mounted cloud head camera and other hardware equipment of the system, which lays a foundation for the software algorithm of the system. 2) the commonly used moving target detection methods are studied and analyzed. Two working modes of the system are determined, that is, the camera is still and the camera is moving. A virtual wire-frame difference method is proposed to detect moving vehicles when the camera is still in common mode. The experimental results show that the method is simple and effective. 3) Research and analysis of common license plate location methods. A license plate location method based on improved haar-like feature and Adaboost classifier is proposed. According to the characteristics of the license plate region, the haar-like feature suitable for the license plate is designed. The Adaboost classifier is trained by a large number of positive and negative samples, and the feature extraction of the detection window is accelerated by integral image. 4) Research and analysis of common license plate tilt correction and character segmentation techniques. According to the characteristic that license plate characters have rich vertical edges and there are few vertical edges around the license plate, a method of license plate tilt correction based on vertical edge feature principal component analysis is proposed. Character segmentation uses license plate character segmentation method based on license plate feature and connected domain analysis. Research and analysis of the commonly used license plate character recognition technology. In this paper, Gabor features and coarse mesh features of characters are extracted firstly, and then BP neural network is used to train a classifier for character recognition of license plate. Based on the experimental results, the author summarizes and analyzes the areas for improvement of the system, and determines the following research work.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;TP391.41

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本文編號(hào)：1924596