Abstract
This paper aims to highlight the problems of license plate detection in real traffic surveillance environment. We notice that existing systems require strong assumptions on license plate norm and environment. We propose a novel solution based on deep learning using self-taught features to localize multi-nation and multi-norm license plates under real road conditions such poor illumination, complex background and several positions. Our method is insensitive to illumination (day, night, sunrise, sunset,...), translation and poses. Despite the low resolution of images collected from real road surveillance environment, a series of experiments shows interesting results and the fastest time processing comparing with traditional algorithms.
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Acknowledgments
Authors would like to express their deepest gratitude to the Tunisian Ministry of the Interior for the opportunity to cooperate with them and to provide the dataset.
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Naimi, A., Kessentini, Y., Hammami, M. (2016). Multi-nation and Multi-norm License Plates Detection in Real Traffic Surveillance Environment Using Deep Learning. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_52
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DOI: https://doi.org/10.1007/978-3-319-46672-9_52
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