Abstract
In this work, we tackle the problem of vehicle license plate localization in traffic images. Rather than modeling the appearance of the license plate, we propose to model the appearance of the corners of the license plate. Inspired by recent advances in human pose estimation, a CNN-based model trained to perform numerical regression is used to infer the location of the four corners that define the limits of the license plate. Coordinate regression is applied by means of Differentiable Spatial to Numerical Transform (DSNT) layer [1]. Preliminary results showed an average localization error of 0.3244 pixels which clearly supports the proposed methodology.
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Acknowledgments
This work was supported in part by the Spanish Ministry of Science, Innovation and Universities under Research Grant DPI2017-90035-R, in part by the Community Region of Madrid (Spain) under Research Grant S2018/EMT-4362 and in part by the Electronic Component Systems for European Leadership Joint Undertaking through the European Union’s H2020 Research and Innovation Program and Germany, Austria, Spain, Italy, Latvia, Belgium, The Netherlands, Sweden, Finland, Lithuania, Czech Republic, Romania, and Norway, under Grant 73746.
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Llorca, D.F. et al. (2020). License Plate Corners Localization Using CNN-Based Regression. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_14
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DOI: https://doi.org/10.1007/978-3-030-45096-0_14
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