外文资料翻译---车牌识别

1、毕业设计 ( 论文 ) 外文资料翻译学院：电子与电气学院专业：电子信息工程姓名：学号：外文出处：License plate recognition(用外文写)Louka Dlagnekov, Serge Belongie附件：1.外文资料翻译译文； 2.外文原文。指导教师评语：签名：年月日注：请将该封面与附件装订成册。附件 1：外文资料翻译译文车牌识别摘要： 车牌号码识别（ LPR）的研究是一个相当重要的问题，这一系统已经是商业运作系统的几个重要组成部分之一。然而许多类似系统需要复杂的视频采集硬件并且需要与红外闪光灯利用技术相结合，用以形成大尺寸车牌在某些区域和（人工）字符鉴别。在本文中，我们

2、描述了一个车牌识别系统，这一系统实现了高质量的视频信号分辨，具备较高的识别率而且不需要昂贵的硬件。我们也探讨了汽车制造和模式识别问题，其目的在于搜寻用于部分车牌号码监控并与录像档案馆联合一些汽车视觉描述系统。我们的提出的方法将提供给民间基础设施宝贵的信息，并提供以各种情境为执法对象的信息。1、简介车牌识别问题（ LPR）被广泛认为是与许多系统急待解决的问题之一。一些较为著名的是为伦敦交通拥挤而设置的收费系统，以及为美国海关边境巡逻任务开发的系统，还有在加拿大和美国的部分收费公路用于执法的收费系统 。虽然向公众发布了一些关于商业的准确性细节，但是部署的车牌识别系统仅仅在可操作的条件下才能正常工作

3、。因而，他们有两个主要缺点是我们可以解决的：首先，他们需要高解析度成像，需要有专门的硬件。大多数的学术研究在这方面也需要高清晰度图像或依赖于特殊地理位置的车牌，并考虑下到这些地区的人和实物因素，甚至是一些常见的误读字符和特殊字符。其次，具有一定性质的 LPR系统可以当作是汽车的指纹车牌。换句话说，确定车辆的身份完全基于附带的车牌。可以想见，这种情况下需要考虑两个板块从完全不同的品牌和型号的汽车被调换的情况，在这种情况下，这些系统将无法发现这一问题。我们作为分辨者也不太会容易分辨汽车的车牌号码， 除非汽车很接近我们的。我们也不能非常容易的记忆所有字符。然而，我们能够识别和记忆的汽车外观，即使当汽

4、车正在加速离开我们。事实上，信息记忆表现出这样了一种迹象，首先是汽车的品牌和型号， 只有那么它的车牌号码， 有时甚至不是一个完整的号码。 因此，考虑到汽车的外观描述和部分车牌号，当局应该能够查询他们对类似的车辆监控系统和检索时，该车辆失踪与当时存档的录像资料以及时间记号。在本文中，我们描述了一个车牌识别方法，执行良好，而且不需要使用昂贵的成像硬件，同时可以用于探索汽车制造商和型号识别（MMR）。由于车牌的互补性和品牌和型号的信息不同，使用时需要考虑分辨能力，不仅具备高精度文字分辨能力，而且具备更强能力的汽车监控系统。2、车牌侦测在任何物体识别系统中， 有两个需要加以解决的重大问题-即检测到场景

5、中的对，并且认识它，检测是一个重要的先决条件。我们进入车牌检测问题则作为一个文本提取的问题 5 。该检测方法可作如下描述。窗口大约一个车牌图像的尺寸，被安置在每个视频流和帧的图像内容作为输入传递给一个分类器，如果窗口包含一个车牌其输出为 1 否则为 0。然后把放置在画面中的车牌和候选地点的所有可能的地点记录该在类并输出一览表。而实际上，这种分类，我们需要一个强大的分类器，存在许多弱分类， 针对于不同功能的专门牌照， 从而使每一个更为准确的分辨出来。这种有效的分类器采用 AdaBoost 算法，因为它们只需要超过 50的准确度， AdaBoost 的选择最佳的现象并从弱分类设置， 每一个单一的功

6、能都能用弱分类器实现。3、品牌和型号识别由于与车牌识别问题，探测车的第一步，品牌和型号进行识别（MMR）。为此，我们可以应用运动分割方法估算的感兴趣区域（ROI），其中包含了汽车。相反，我们决定使用作为存在和一个视频流中车辆的位置显示检测到的车牌位置，并为作物识别汽车的投资回报率。这种方法也将是有益品牌和型号的静态影像，那里的分割问题是比较困难的。3.1 字符识别应用二值化算法，这是我们最初的意图，如对尼布莱克算法的修改后的版本由陈和尤尔 5 用于提取牌照从我们的检测器板的图像，然后使用商业二元化的形象OCR的包作为输入。我们发现，即使我们尝试在104 × 31 的 OCR软件包决议

7、也产生了非常糟糕的结果。也许这不应该作为许多定制的OCR车牌识别系统在现有的解决方案考虑。除非在阅读文字手写的形式, 然后对分割图像进行识别，这是与OCR软件来分割共同的特点。市场细分最简单的方法通常涉及的行和列像素，并把部门在当地最小的投影功能的投影。在我们的数据中，分辨率太低可依靠分割字符以这种方式解决，因此，我们决定采用模板匹配，而不是简单的匹配，它可以同时找到两个位置的人物和他们的身份。该算法可描述如下：对于每个字符的例子，我们搜索在车牌图像模板的图像中所有可能的偏移量，并记录前 N 最佳匹配。的搜索是通过使用标准化互相关（ NCC）的，以及关于评分阈值的位置，然后才考虑一个可能匹配的

8、应用。如果有多个字符匹配一个地区的平均字符的大小，选择具有较高的相关性特征，具有较低的相关性字符将被丢弃。一旦所有的模板已检索，发现每个地区的特点是从左向右形成一个字符串。 N 是对车牌图像分辨率的依赖性，应当选择恰当，并不是所有的 N 场比赛是在一个单一的字符相同的字符时，多次出现在图像里，所以，并非所有地区都可能处理。 这种方法似乎效率不高， 但是，认识过程的一类二阶时的时间为 104 × 31 分辨率，我们是可以接受的这一范围的。4、数据集我们会自动生成运行在几个小时的视频数据检测和跟踪车牌和种植 400 × 220 像素周围的每个跟踪序列中的车牌架固定窗口的大小的汽

9、车图像数据库。这种方法产生的图像，其中 1,140 辆各品牌和型号的大小都大致相同。该作物窗口位置，这样的车牌是在底部的第三个中心的外形。我们选择这个作为参考点，以确保匹配的正确性，完成的只有车的提取而不是背景的提取。如果我们为中心的车牌纵向和横向的汽车，车牌装在他们的保险杠会在图像中出现道路的图像。在收集这些图片，我们手动指定品牌，型号和年份标签的 1,140 张图片中的 790。我们无法标签其余 350 张图像由于我们与这些汽车不很熟悉的。我们经常做的汽车的网站来确定汽车的制造和使用。该网站允许用户输入检查汽车号码检测是否已通过最近的烟雾检查。对于每个查询，该网站返回烟雾的历史以及汽车的品

10、牌和型号说明如果可用。美国加利福尼亚州要求所有车辆超过三年以上才能通过烟雾检查每二年。因此，对我们个人的经验而言，他们依靠标签查询汽车。我们分成查询设置 1,140 标记图像和数据库设置。查询集包含选择代表了 38 多种品牌和型号的图像类， 与相同品牌和型号， 但不同年份多个查询在某些情况下，为了捕捉随着时间的推移变化的模型设计。我们评估在数据库中找到的查询每个图像的最佳匹配的识别方法每场演出。4.1 SIFT特征匹配尺度不变特征变换（ SIFT）特征洛韦最近开发的是不变的规模，甚至部分不变旋转和光照差异，这使得它们也适合用于识别物体。我们采用SIFT 特征匹配的孕产妇死亡率问题如下：1为每个

11、图像 D 的数据库和查询影像q 时，执行关键点定位和描述符的任务。2. 对于每个数据库图像 D：（一）对于每一个关键点克勤Q中找到关键点在 D 第纳尔具有最小的L2 距离，并至少有一个最近的描述距离较小的因素。如果没有这样的科威特第纳尔存在，检查下克勤的理论不成立。（二）计数的 n 的成功匹配在描述号码d.3. e 选择具有最大的 N 和认为的最佳匹配。5、结果对 SIFT 匹配算法产生上述的查询设置了 89.5 的识别率。对于在集合测试一些疑问识别结果显示在图 6。前 10 场比赛都是同一品牌和超过 20 的数据库中的一些类似的模型车的所有疑问。SIFT 特征匹配的查询大部分无法正确分类有5

12、 个或更少的条目类似的数据库了。对制造和相应的查询与数据库中的许多例子模型的结果，它是安全的假设，拥有品牌和型号，每类将提高识别率更多的例子。附件 2：外文原文（复印件）License plate recognitionAbstractLicense Plate Recognition (LPR) is a fairly well explored problem and is already a component of several commercially operational systems. Many of these systems, however, require soph

13、isticated video capture hardware possibly combined with infrared strobe lights or exploit the large size oflicense plates in certain geographical regions and the (artificially) high discriminability of characters. In this paper,we describe an LPR system that achieves a high recognition rate without

14、the need for a high quality video signalfrom expensive hardware. We also explore the problem of car make and model recognition for purposes of searchingsurveillance video archives for a partial license plate number combined with some visual description of a car. Our proposed methods will provide val

15、uable situational information for law enforcement units in a variety of civil infrastructures.1 IntroductionLicense plate recognition (LPR) is widely regarded to be a solved problem with many systems already in operation.Some well-known settings are the London Congestion Charge program in Central Lo

16、ndon, border patrol duties by the U.S. Customs, and toll road enforcement in parts of Canada and the United States. Although few details are released to the public about the accuracy of commercially deployed LPR systems, it is known that they work well under controlled conditions. However, they have

17、 two main disadvantages which we address in this paper.Firstly, they require high-resolution and sometimes specialized imaging hardware. Most of the academic researchin this area also requires high-resolution images or relies on geographically-specific license plates and takes advantageof the large

18、spacing between characters in those regions and even the special characterfeatures of commonly misread characters.Secondly, LPR systems by their nature treat license plates as cars fingerprints. In otherwords, they determine a vehicle s identity based solely on the plate attached to it. Onecan imagi

19、ne, however, a circumstance where two plates from completely different make and model cars are swapped with malicious intent, in which case these systems would notfind a problem. We as humans are also not very good at reading cars license platesunless they are quite near us, nor are we very good at

20、remembering all the characters.However, we are good at identifying and remembering the appearance of cars, andtherefore their makes and models, even when they are speeding away from us.In fact, the first bit of information Amber Alert signs show is the car s make aand only then its license plate num

21、ber, sometimes not even a complete number.Therefore, given the description of a car and a partial license plate number, the authorities should be able to query their surveillance systems for similar vehicles and retrieve a timestamp of when that vehicle was last seen along with archived video data f

22、or that time. In this paper, we describe an LPR method that performs well without the need for expensive imaging hardware and also explore car make and model recognition (MMR). Because of the complementary nature of license plate and make and model information, the use of MMR can not only boost the

23、LPR accuracy, but allow for a more robust car surveillance system.2 License Plate DetectionIn any object recognition system, there are two major problems that need to be solved that of detecting an object in a scene and that of recognizing it; detection being an important requisite. We approached th

24、e license plate detection problem as a text extraction problem 5. The detection method can be described as follows. A window of interest, of roughly the dimensions of a license plate image, is placed over each frame of the video stream and its image contents are passed as input to a classifier whose

25、 output is 1 if the window appears to contain a license plate and 0 otherwise. The window is then placed over all possible locations in the frame and candidate license plate locations are recorded for which the classifier outputs a1. In reality, this classifier, which we shall call a strong classifi

26、er, weighs the decisions of many weak classifiers, each specialized for adifferent feature of license plates, thereby making a much more accurate decision. This strong classifier is trained using the AdaBoost algorithm, and the weak classifiers are considered weak since they only need be over 50% ac

27、curate. Over several rounds, AdaBoost selects the best performing weak classifier from a set of weak classifiers, each acting on a single feature.3 Make and Model RecognitionAs with the license plate recognition problem, detecting the car is the first step to performing make and model recognition (M

28、MR). To this end, one can apply a motion segmentation method to estimate a region of interest (ROI) containing the car. Instead, we decided to use the location of detected license plates as an indication of the presence and location of a car in the video stream and to crop an ROI of the car for reco

29、gnition. This method would also be useful for make and model recognition in static images, where the segmentation problem is more difficult.3.1 Character RecognitionIt was our initial intent to apply a binarization algorithm, such as a modified version of Niblack s algorithm as used by Chen and Yuil

30、le 5, on the extracted license plate images from our detector, and then use the binarized image as input to a commercial OCRpackage. We found, however, that even at a resolution of 104 31 the OCR packages× we experimented with yielded very poor results. Perhaps this should not come as a surpris

31、e considering the many custom OCR solutions used in existing LPR systems.Unless text to be read is in hand-written form, it is common for OCR software to segment the characters and then perform recognition on the segmented image. The simplest methods for segmentation usually involve the projection o

32、f row and column pixels and placing divisions at local minima of the projection functions. In our data, the resolution is too low to segment characters reliably in this fashion, and we therefore decided to apply simple template matching instead, which can simultaneously find both the location of cha

33、racters and their identity.The algorithm can be described as follows. For each example of each character, we search all possible offsets of the template image in the license plate image and record the top N best matches. The searching is done using normalized cross correlation (NCC), anda threshold

34、on the NCC score is applied before considering a location a possible match. If more than one character matches a region the size of the average character, the character with the higher correlation is chosen and the character with the lower correlation is discarded. Once all templates have been searc

35、hed, the characters for each region found are read left to right forming a string. N is dependent on the resolution of the license plate image and should be chosen such that not all N matches are around a single character when the same character occurs more than once on a plate, and not too large so

36、 that not all possible regions are processed.This method may seem inefficient, however, the recognition process takes on the order of half a second for a resolution of 104 31, which we× found to be acceptable.4 DatasetsWe automatically generated a database of car images by running our license p

37、late detector and tracker on several hours of video data and cropping a fixed window of size400 ×220 pixels around the license plate of the middle frame of each tracked sequence. This method yielded 1,140 images in which cars of each make and model were of roughly the same size. The crop window

38、 was positioned such that the license plate was centered in the bottom third of the image. We chose this position as a reference point to ensure matching was done with only car features and not background features. Had we centered the license plate both vertically and horizontally, cars that have th

39、eir plates mounted on their bumper would have exposed the road in the image.After collecting these images, we manually assigned make, model, and year labels to 790of the 1,140 images. We were unable to label the remaining 350 images due to our limited familiarity with those cars. We often made use o

40、f the California Department ofMotor Vehicles web site to determine the makes and models of cars with which we were not familiar. The web site allows users to enter a license plate or vehicle identification number for the purposes of checking whether or not a car has passed recent smog checks.For eac

41、h query, the web site returns smog history as well as the car s make and m description if available. The State of California requires all vehicles older than threeyears to pass a smog check every two years. Therefore, we were unable to query cars that were three years old or newer and relied on our

42、personal experience to label them.We split the 1,140 labeled images into a query set and a database set. The query set contains 38 images chosen to represent a variety of make and model classes, in some cases with multiple queries of the same make and model but different year in order to capture the variation of model designs over time. We evaluated the performance of each of the recognition methods by finding the best match in the database for each of the query images.4.1 SIFT MatchingScale invari