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Improved License Plate Recognition for Low-Resolution CCTV Forensics by Integrating Sparse Representation-Based Super-Resolution

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Digital-Forensics and Watermarking (IWDW 2013)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 8389))

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Abstract

Automatic license plate recognition (LPR) is an important functionality for closed-circuit television (CCTV) forensics. However, uncontrolled capture conditions make it still difficult to achieve effective LPR in practice. In this paper, we propose a novel method for robust LPR in real-world imagery, leveraging sparse representation-based (SR-based) super-resolution. To that end, we make use of high-resolution license plate (LP) images that are used for both (1) the construction of a dictionary for SR-based super-resolution and (2) the training of LP character classifiers. Comparative experimental results indicate that the proposed SR-based super-resolution method allows for effective LPR in low-resolution imagery captured by long-distance CCTV cameras.

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Acknowledgment

This work was supported by a grant from the National Research Foundation (NRF) of Korea (grant number: NRF-2012K2A1A2033054).

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Authors and Affiliations

  1. Image and Video Systems Lab, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea

    Hyun-seok Min, Seung Ho Lee, Wesley De Neve & Yong Man Ro

  2. Multimedia Lab, Ghent University – iMinds, Gaston Crommenlaan 8 bus 201, 9050, Ledeberg-Ghent, Belgium

    Wesley De Neve

Authors
  1. Hyun-seok Min
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  2. Seung Ho Lee
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  3. Wesley De Neve
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  4. Yong Man Ro
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Corresponding author

Correspondence to Hyun-seok Min .

Editor information

Editors and Affiliations

  1. New Jersey Institute of Technology, Newark, New Jersey, USA

    Yun Qing Shi

  2. CIST, Korea University, Seoul, Korea, Republic of (South Korea)

    Hyoung-Joong Kim

  3. EE Telecomunicacion, University of Vigo, Vigo, Spain

    Fernando Pérez-González

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Min, Hs., Lee, S.H., De Neve, W., Ro, Y.M. (2014). Improved License Plate Recognition for Low-Resolution CCTV Forensics by Integrating Sparse Representation-Based Super-Resolution. In: Shi, Y., Kim, HJ., Pérez-González, F. (eds) Digital-Forensics and Watermarking. IWDW 2013. Lecture Notes in Computer Science(), vol 8389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43886-2_32

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  • DOI: https://doi.org/10.1007/978-3-662-43886-2_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43885-5

  • Online ISBN: 978-3-662-43886-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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