III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

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WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.Figure 1.1: Example of an image vehicleidentification system.Nowadays, a bunch of companies and R&D centerswork or offer automatic vehicle license platerecognition systems. However, it is difficult to make acomparative analysis among them, as they do notfollow any standard in terms of features or performancemeasurement.The objective of this work is to present anautomatic system that uses image pre-processing forfeature extraction and hypothesis making based on amatched filter to detect the car license plate image fromdigital images in which the license plate image can befound. Specifically the digital images used in this workare acquired in a Brazilian highway with intensevehicle flow and are restricted to private cars. From thedetected images, the corresponding license plate can beidentified in a second step. This step has beenpreviously performed in [2].2. System DevelopmentIn this section, the localization system will bespecified based on previous studies and according tothe Brazilian responsible department resolutions(CONTRAN). After that, it will be shown how theimages were acquired and how the database wascreated.Typically, a vehicle license plate identificationsystem comprises 5 sequential stages:(i) Vehicle image acquisition;(ii) License plate localization;(iii) License plate character localization;(iv) Individual character identification;(v) License plate identification.Based on this classical division of stages, this workaims to present a set of techniques for the two firstphases, that is, vehicle image acquisition, under realconditions, and the following stage, license platelocalization.In this project, the system was designed to acquirethe images, store them in a database and process themoffline. So, according to this specification, the systemhas the following architecture:Figure 2.1: System architecture.The database comprised 1250 images and was splitinto two sets: the development subset, with 90% of theentire database, and the testing subset, formed by theremaining 10% of the images.2.1. Image acquisitionThe image acquisition was done in an express laneof toll area during two days. It was necessary to put ananalog camera connected to a DAQ board that wasinstalled in a PC. The system trigger signal wasgenerated by an inductive sensor installed on the lanefloor.The acquisition was done continuously through thatentire period, generating more than 25.000 images. Notall the images fit the interest of this work, once in someof the following problems occurred:(i) Images with no car;(ii) Car images without license plates;(iii) Images with incomplete license plate;(iv) Images out of focus;(v) Official license plates, buses license plates andso on – there are lot of types of license plates, but notall of them are from private vehicles (whose charactersare black and the background gray), object of ourstudy.Below there is a sample extracted from the databaseused in this work:Figure 2.2: Database sample187
WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.Those problems, together with the need of knowingthe exact position of the license plate and its charactersin the image, motivated the constructions of a databasedescribed below.2.3. Database creationWith the objective of creating the database, it wasnecessary to classify the images as well as register thespatial information of interest. To aid in this task, itwas developed a system to register the images [3]. Theinformation of interest for the classification of eachimage is:FieldFile NameLicense Plate in theImageContentIndicates the image filelocationComplete, partial, noneLicense Plate QualityGood, with shadows,bashed, illegibleBumper Contrast Dark, lightLicense Plate Type Gray, red, othersLicense Plate NumberASCII string withlicense plate characters(x,y) location of the dotDotthat separates numbers andletters (in pixel)Top-left (x,y) andCharactersbottom-right (x,y)characters positioncoordinates (in pixel)Top-left (x,y) andPlatebottom-right (x,y) licenseplate position coordinates(in pixel)Table 2.1: Registered fields for each image.Until the beginning of this work, 5.000 images hadbeen registered. Within this group, 1.250 images wereclassified as Gray, Complete and Good. This work wasdeveloped with these 1.250 images, pointing out that90% of these images (1125) were used as thedevelopment set and 10% (125), chosen randomly,were used as the test set of images.3. Matched Filter DesignThe work was developed on an off-line set ofimages that were previously inserted into the database.The methodology is based on classical signalprocessing techniques (instead of classical imageprocessing ones) used in sequence. The first stage, thepre-processing, horizontal frequency filtering, is usedto reduce the amount of area that the matched filter (thesecond stage) will act on.The expected matched filter output is the licenseplate region. On these outputs the efficiency studieswill be performed.3.1. Pre-processingThe pre-processing goal is to reduce the area thatthe matched filter will search for the license plateimage. With that approach some benefits are intendedto be achieved, such as speeding up the system, oncethe matched filter will not need to search the entireimage, a high computational cost operation, andimproving the detection efficiency because noiseregions will be eliminated.Instead of classical image processing filters, wedecided to detect the license plate image through itscharacteristic horizontal frequency [1], what isassociated to inter-character spacing, constant inBrazilian license plates, following CONTRAN rules.In order to find out the frequency region that thecharacteristic horizontal frequency falls within, somelicense plate images were analyzed. The figure belowillustrates the analysis of a single plate:Figure 3.1: Characteristic horizontal frequencyanalyses in a single license plate.Three lines that pass through all the characters (inblue, red and gray) were chosen and analyzed. Eachpair of graphs represents the Luminance x Pixelfunction (graph above) and its FFT coefficients (graphbelow).After concluding the analysis for the set of licenseplate chosen, a cumulative graph (the sum of every188
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ApresentaçãoA área de Visão Com
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Automatic Pattern Recognition of Bi
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