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System Locating License Plates with Shadow Based on Self-adaptive Window Size Technique

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Geo-Spatial Knowledge and Intelligence (GRMSE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 699))

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Abstract

Most of the existing license plate localization algorithms have a parameter that is related to the size of the license plate. There is no parameter that is suitable for all the cases. In this paper, an algorithm is proposed to automatically compute the size-related parameter. Then a hierarchical system based on the self-adaptive parameter is proposed to locate license plates. Both connected component based methods and vertical edge based methods are used. The parameter is first used as the local window size to suppress the shadow. Then it is used to connect the discrete vertical edges to form a license plate region. The proposed system is used to locate license plates with shadow, and experiments are taken on images with different resolutions. The total localization accuracy achieves 94.40%. It can compete with the state-of-the-art methods and need not determine the optimal parameter by trial and error.

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Acknowledgments

This research work was supported by China Natural Science Foundation (No: 61272304) and Zhejiang Provincial Natural Science Foundation of China (No. LY15F020024).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Jingyu Dun & Sanyuan Zhang

Authors
  1. Jingyu Dun
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  2. Sanyuan Zhang
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Corresponding author

Correspondence to Jingyu Dun .

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Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Hanning Yuan

  2. Beijing Institute of Technology, Beijing, China

    Jing Geng

  3. Wuhan University, Wuhan, China

    Fuling Bian

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Dun, J., Zhang, S. (2017). System Locating License Plates with Shadow Based on Self-adaptive Window Size Technique. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-3969-0_15

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  • DOI: https://doi.org/10.1007/978-981-10-3969-0_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3968-3

  • Online ISBN: 978-981-10-3969-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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