【正文】 ture treat license plates as cars’ fingerprints. In other words, they determine a vehicle’s identity based solely on the plate attached to it. One can imagine, however, a circumstance where two plates from pletely different make and model cars are swapped with malicious intent, in which case these systems would not find a problem. We as humans are also not very good at reading cars’ license plates unless they are quite near us, nor are we very good at remembering all the characters. However, we are good at identifying and remembering the appearance of cars, and therefore 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 and model and only then its license plate number, sometimes not even a plete 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 for 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 plementary nature of license plate and make and model information, the use of MMR can not only boost the LPR accuracy, but allow for a more robust car surveillance system. 2 License Plate Detection In 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。美國加利福尼亞州要求所有車輛超過三年以上才能通過煙霧檢查每二年。 該算法可描述如下 ： 對(duì)于每個(gè)字符的例子，我們搜索在車牌圖像模板的圖像中所有可能的偏移量，并記錄前 N 最佳匹配。我們 進(jìn)入 車牌檢測問題 則 作為一個(gè)文本提取的問題 [5]。 簡介 車牌識(shí)別問題（ LPR）被廣泛認(rèn)為是與許多系統(tǒng)急待解決的問題之一。一些較為著名的是為倫敦交通擁擠而設(shè)置的收費(fèi)系統(tǒng)，以及為美國海關(guān)邊境巡邏任務(wù)開發(fā)的系統(tǒng)，還有在加拿大和美國的部分收費(fèi)公路用于執(zhí)法的收費(fèi)系統(tǒng) 。該檢測方法可 作 如下描述。的搜索是通過使用標(biāo)準(zhǔn)化互相關(guān)（ NCC）的，以及關(guān)于評(píng)分閾值的位置，然后才考慮一個(gè) 可能匹配 的 應(yīng)用。因此，對(duì)我們個(gè)人的經(jīng)驗(yàn) 而言 ，他們依靠標(biāo)簽查詢汽車。 detection being an important requisite. We approached the 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 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 classifier, weighs the decisions of many weak classifiers, each specialized for a different 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% accurate. Over several rounds, AdaBoost s