导　　师： 吴忻生

学科专业： 081101

授予学位： 硕士

作　　者： ;

机构地区： 华南理工大学

摘　　要： 智能交通系统（Intelligent Transportation System，ITS）是目前世界交通运输领域一项重要的课题，它将先进的信息技术、数据传输技术、电子传感技术及计算机技术等有效地运用于地面交通管理系统,已经成为21世纪现代化交通运输体系的重要发展方向。而对交通路况进行监控，快速准确地获取各种交通参数，是实施ITS系统管理的前提，因此基于视频图像的智能交通监控系统的重要性日益突出，其主要研究目标是对交通目标进行检测、识别和跟踪，并对其行为进行分析和判断，以便获得重要的路况状态信息，实现交通管理的智能化。本文主要研究了智能交通系统中目标车辆识别的方法以及车辆粘连时的分割方法。 1在已有研究的基础上，分析了Haar-like矩形特征的组成形式，并引入积分图像对矩形特征的特征值进行快速地计算。 2研究及实现了一种基于Haar-like矩形特征的道路车辆识别方法，利用Haar-like矩形特征来表达车辆，采用Adaboost算法训练车辆分类器。针对Adaboost算法训练过程迭代耗时的缺点，提出了一种基于样本马氏距离的分类器选取方法，根据正负样本的概率分布情况来求取所选取的分类器的阈值和偏置，然后分区段对样本的权重进行更新。该算法大大减少了算法训练过程的复杂度。 3针对复杂交通环境中车辆粘连的问题，提出了一种基于凹性分析的粘连车辆分割方法。对于检测出的感兴趣区域，首先判断是否存在粘连，若存在，则采用扫描的方法来寻求分割点，并结合一系列的约束条件来选择最优分割线。该方法快速的将粘连车辆分割开，有效地解决了车辆粘连的问题。 Intelligent Transportation System /(ITS/) is the key issue of the current worldtransportation field. It integrates advanced information technology, data communicationtechnology, electronic sensing technology, computer technology and all kinds of otheradvanced technologies synthetically and applies them to the ground traffic managementsystem, and has become an important development aspect of the modernized transportationsystem in21st century. While getting accurate real-time traffic parameters by videosurveillance method is a prerequisite for intelligent traffic management,the importance of intelligent traffic monitoring system based on video images, whose mainresearch target is to detect, recognize and track vehicles and analyze there behaviors so thatwe can get the important road information to make the traffic management intelligent, havebecome increasingly prominent. This paper mainly the approach of target vehicle recognition and the method how to splitadhesion vehicles, and the main works are as follows: Firstly, Base on the previous research, the structure of Haar-like rectangular feature andthe integral image which can compute the value of the feature is analyzed. Secondly, we studied a road vehicle method based on Haar-like feature. We useHaar-like features to describe vehicle feature and construct weak classifiers，and AdaBoostalgorithm is chosen to train the vehicle classifiers. Focusing on analysis of the trainingprocess of Adaboost algorithm, and against the time-consuming in the iterative processing,we proposed a sample’s Mahalanobis distance-based classifier selection methods. Meanwhile,compute the chosen classifier’s threshold and error rate based on the probability distributionof the positive and negative samples. And then update sample’s weights among the partition.The propose algorithm greatly reduces the complexity of the training process. Finally, this paper proposed a solution to the problem of vehicles adhesion: a regionsplitting method based on concavity analysis. Fir

关 键 词： 智能交通系统 车辆识别 马氏距离 粘连 车辆分割

领　　域： [自动化与计算机技术] [自动化与计算机技术]