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

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