Efficient scale-adaptive license plate detection system

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dc.contributor.author Molina Moreno, Miguel
dc.contributor.author González Díaz, Iván
dc.contributor.author Díaz de María, Fernando
dc.date.accessioned 2020-09-22T07:30:09Z
dc.date.available 2020-09-22T07:30:09Z
dc.date.issued 2019-06-01
dc.identifier.bibliographicCitation IEEE Transactions on Intelligent Transportation Systems, (2019), 20(6), pp.: 2109-2121.
dc.identifier.issn 1524-9050
dc.identifier.uri http://hdl.handle.net/10016/30842
dc.description.abstract License plate detection is a common problem in traffic surveillance applications. Although some solutions have been proposed in the literature, their success is usually restricted to very specific scenarios, with their performance dropping in more demanding conditions. One of the main challenges to be addressed for this kind of systems is the varying scale of the license plates, which depends on the distance between the vehicles and the camera. Traditionally, systems have handled this issue by sequentially running single-scale detectors over a pyramid of images. This approach, although simplifies the training process, requires as many evaluations as considered scales, which leads to running times that grow linearly with the number of scales considered. In this paper, we propose a scale-adaptive deformable part-based model which, based on a well-known boosting algorithm, automatically models scale during the training phase by selecting the most prominent features at each scale and notably reduces the test detection time by avoiding the evaluation at different scales. In addition, our method incorporates an empirically constrained-deformation model that adapts to different levels of deformation shown by distinct local features within license plates. As shown in the experimental section, the proposed detector is robust and scale and perspective independent and can work in quite diverse scenarios. Experiments on two datasets show that the proposed method achieves a significantly better performance in comparison with other methods of the state of the art.
dc.description.sponsorship This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grant TEC2014-53390-P and Grant TEC2017-84395-P and in part by the Spanish Department of Traffic, Spanish Ministry of Home Affairs, under Grant SPIP2014-1507.
dc.format.extent 12
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
dc.subject.other License plate detection
dc.subject.other GentleBoost
dc.subject.other Scale-adaptive part-based model
dc.subject.other Video surveillance
dc.title Efficient scale-adaptive license plate detection system
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TITS.2018.2859035
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2014-53390-P
dc.relation.projectID Gobierno de España. TEC2017-84395-P
dc.relation.projectID Gobierno de España. SPIP2014-1507
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 2109
dc.identifier.publicationissue 6
dc.identifier.publicationissue Publicado
dc.identifier.publicationlastpage 2121
dc.identifier.publicationtitle IEEE Transactions on Intelligent Transportation Systems
dc.identifier.publicationvolume 20
dc.identifier.uxxi AR/0000023861
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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