Visual Attention Saliency Model for License Plate Location

Gu, Boyu

doi:10.14257/ijsip.2015.8.2.26

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…Very recently, a small number of works have been published in using saliency detection for ALPR research, such as the works carried out sby Gu et al [31] and Lin et al [32]. Using the algorithm in Ref.…”

Section: Comparison Of the Proposed Methods With Other Methodsmentioning

confidence: 99%

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Robust Search-Free Car Number Plate Localization Incorporating Hierarchical Saliency

Safaei¹,

HL²

2016

J Comput Sci Syst Biol

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There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1-norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars. A detection accuracy of 94.77% and an average execution time of 40 ms for 600 × 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost

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Section: Comparison Of the Proposed Methods With Other Methodsmentioning

confidence: 99%

“…The number of features in Ref. [31] is thirty, which makes their estimation time-consuming, and the classifier is very likely to be over-fitted. A similar approach Lin et al [32] filters the image using difference of Gaussian (DoG).…”

Section: Comparison Of the Proposed Methods With Other Methodsmentioning

confidence: 99%