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1、此文檔是畢業(yè)設(shè)計外文翻譯成品( 含英文原文+中文翻譯) ,無需調(diào)整復(fù)雜的格式!下載之后直接可用,方便快捷!本文價格不貴,也就幾十塊錢!一輩子也就一次的事!外文標(biāo)題:A Real Time Vehicle’s License Plate Recognition System外文作者:Choudhury A. Rahman, Wael Badawy,Ahmad Radmanesh文獻出處: Proceedings of the IEEE C
2、onference on Advanced Video and Signal Based Surveillance, 2018. (如覺得年份太老,可改為近 2 年,畢竟很多畢業(yè)生都這樣做)英文 2576 單詞,15730 字符(字符就是印刷符),中文 4171 漢字。 (如果字?jǐn)?shù)多了,可自行刪減,大多數(shù)學(xué)校都是要求選取外文的一部分內(nèi)容進行翻譯的。 )A Real Time Vehicle’s License Plate Recogni
3、tion SystemAbstract: A smart and simple algorithm is presented in this paper for vehicle’s license plate recognition system. Based on pattern matching, this algorithm can be applied for real time detection of license pla
4、tes for collecting data for surveying or for some application specific purposes. The proposed system has been prototyped using C++ and the experimental results have been shown for recognition of Alberta license plates.1.
5、IntroductionVehicle’s license plate recognition system has been a special area of interest in video surveillance arena for more than a decade or so. With the advent of sophisticated video vehicle detection systems for tr
6、affic management applications, number plate recognition system finds wide varieties of places to fit itself beyond just controlling access to a toll collection point or parking lot. It can now be integrated to the video
7、vehicle detection systems which usually are installed in places of interest for intersection control, traffic monitoring etc., to identify vehicle that violates traffic laws or to find stolen vehicles. There are a number
8、 of techniques used so far for recognition of number plates such as BAM (Bi-directional Associative Memories) neural network character recognition [1], pattern matching [2] etc. The technique used in the proposed system
9、is based on pattern matching, which is fast and accurate Almost all the algorithms developed so far work by following similar steps. Seven general processing steps have been identified as being common to all number plate
10、 recognition algorithms [3]. These are:Trigger: This may be hardware or software trigger. Hardware trigger is the old approach where inductive loop is used for triggering and this tells when the image should be captured
11、by detecting the presence of vehicle. Hardware trigger is now-a-days being replaced by software trigger and in operational in many places. In software trigger, image is divided into zones and by image analysis the detect
12、ion of vehicles is performed.Image Capture: Hardware or software trigger activates the image capturing device to capture and store image for further analysis.Vehicle’s Presence: This step is needed only if trigger is don
13、e after definite time intervals without knowing that a vehicle is present in the captured image. This step compares the captured image with the background image and detects if there is any significant change. If not, the
14、 captured image is just ignored, otherwise it moves to the next step.Finding Plate: This step is for locating the number plate in the captured image. A number of techniques can be used in this step e.g. color detection [
15、4], signature analysis [5], edge detection [6] etc. Any tilt in the captured image is corrected in this step. Once the number plate is located the image is ready for character recognition.Character segmentation: Segmenta
16、tion can be done by detecting the transition from dark to light or from light to dark level. Each character present in the number plate produces a band of gray level. So by detecting the similar gray level bands each cha
17、racter can be segmented.Recognition process: This is the OCR step. The techniques that can be used in this step include pattern matching [2], feature matching [7][8] and neural network classifier [9].Post process: This i
18、s the application specific process step. Depending on the application this step may save the detected number plate for traffic data collection, try to match the number plate to the stolen vehicles database or to open the
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