CASIA OpenIR  > 毕业生  > 硕士学位论文
假牌车智能侦别算法研究和系统实现
Alternative TitleAlgorithms in Fake License Plate Detection System
张帆
Subtype工学硕士
Thesis Advisor常红星
2013-05-31
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword违章车辆监控 车标检测 视觉显著性 模板匹配 假牌车智能侦别系统 Illegal Vehicle Monitoring Vehicle Logo Detection Visual Saliency Template Matching Fake License Plate Detection System
Abstract随着我国汽车数量的不断增多,传统的人力管理由于效率低下,已经无法满足高密度的现代交通需求。以车辆车牌识别为主要手段的视频道路监控技术,是现代智能交通系统(Intelligent Transportation System, ITS)最重要的一环,在监管违章车辆、维持良好的交通 秩序工作中起到了决定性的作用。然而假牌车可以逃过该系统的监控,同时会给被套牌车主以及车辆管理单位带来许多麻烦,造成不小的社会影响。假牌车智能侦别系统(Fake License Plate Detection System,FLPDS)是解决该问题的途径之一。本文基于图像匹配技术 对假牌车智能侦别算法进行研究,并以此提出了假牌车智能侦别系统的架构。主要工作包括以下三部分内容:基于视觉显著性的汽车标志快速提取、汽车标志的匹配检验以及假牌车智能侦别系统的结构流程。 汽车标志快速提取过程中,采用一种简单视觉显著性模型,快速区分交通卡口拍摄图片中的车辆标志部分和无关背景部分,达到快速提取感兴趣区域(ROI)、大幅减小处理时间的目的。 汽车标志的匹配检验是整个系统算法的核心部分,本文提出了一种在真实场景下普适的高精度匹配算法。利用车辆标志边缘突出的特点,首先采用边缘特征提取算法提取模板图片和目标图片的边缘信息作为特征,之后根据本文提出的匹配评分算法计算出目标车标与模板的匹配评分,最后与根据大量实验得到的阈值相比较,判断该车辆是否为假牌车。 在实际应用中,首先自然环境复杂多变,匹配算法难免会有误报,同时制造使用假牌是一种较严重的违法行为,因此假牌车智能侦别系统应该包括疑似假牌车辆人工判别模块。其次由于车流量巨大,人力资源有限,系统的识别率必须保持在一个较高的水准,这就要求系统能够适应尽可能多的交通状况(如光线、视角变化、镜头畸变),再结合该算法的特性,本文采用了多模板匹配的方式来提高识别率。实验表明本文提出的检测算法和系统能够很好的结合,达到令人满意的效果。
Other AbstractThe amount of cars in China is largely growing and traditional human management inefficiencies have been unable to keep up with such a high density of modern traffic. Video trafficmonitoring technology, which is mainly based on vehicle license plate recognition, is the important part of the modern Intelligent Transportation System(ITS) and has played a decisive role in illegal vehicle monitoring and maintaining good traffic order. However, illegal vehicles with fake license can escape monitoring of the system, and a lot of trouble will be brought to the true license plate owner and vehicle management bureau. Fake License Plate Detection System(FLPDS) is one of the ways to solve this problem. In this paper, some researches on detection of fake license plate of vehicle based on image matching are done and the architecture of FLPDS is proposed. These researches consist of three parts: fast localization of vehicle logo based on visual saliency, vehicle logo detection and architecture of FLPDS. In the first part, we use a simple visual saliency model to distinguish the small part including vehicle logo against the irrelevant background, thus achieving the purpose of rapid extraction of regions of interest(ROI) and significant reduction of processing time. Then in the second part, a universal high-precision matching algorithm, which is the core part of this paper, is introduced. We extract the feature points of both templates and vehicle images first by a proposed edge-feature extraction method, then we use them to compute a matching score which indicates the matching degree by our metric proposed in this paper. Finally the score is compared with a threshold(which is obtained by large amount of experiments) to decide whether the target license plate is fake. In practical scenes, challenges for vehicle logo matching are mainly from the fact that vehicle logo differs a lot due to changes in viewpoint, deformation and light condition. Leak recognition and mistake recognition are inevitable but we should reduce them to a very low level for two reasons. On the one hand, the behavior of using fake license plates is illegal and the judgement should be prudent, human judgement module should be introduced in the system. On the other hand, due to the huge traffic, there is not enough human resource to deal with suspected vehicles with fake license plate if leak recognition rate is now low enough. Thus Multi-template matching module is recommended in this paper. The sy...
shelfnumXWLW1912
Other Identifier201028014629086
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7656
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
张帆. 假牌车智能侦别算法研究和系统实现[D]. 中国科学院自动化研究所. 中国科学院大学,2013.
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