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Deep Representation Learning for License Plate Recognition in Low Quality Video Images

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Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14362))

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Abstract

License plate recognition is an important technology in many application scenarios such as traffic monitoring and vehicle management. Due to variations of viewpoint, illumination, motion-blur, and degradation in imaging process, it is still a challenging problem to detect and recognize license plates in low quality video images. In this paper, we focus on efficient deep representation learning for license plate recognition, detection and tracking. For license plate recognition, we mainly investigate the configuration of different network structures, and propose to use a network structure with a Convolutional Neural Network (CNN) backbone, an Long Short-Term Memory (LSTM) encoder and a Transformer decoder. For license plate detection, a Transformer encoder-decoder based method is adopted. For license plate tracking, a multi-object tracking method is incorporated by using Kalman filtering and temporal matching to associate detected license plates in video frames. Experiments are carried out on the public large-scale video-based license plate dataset (LSV-LP) to validate the proposed methods.

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Authors and Affiliations

  1. Bei**g National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Bei**g, China

    Kemeng Zhao, Liangrui Peng, Ning Ding, Gang Yao, Pei Tang & Sheng** Wang

Authors
  1. Kemeng Zhao
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  2. Liangrui Peng
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  3. Ning Ding
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  4. Gang Yao
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  5. Pei Tang
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  6. Sheng** Wang
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Corresponding author

Correspondence to Liangrui Peng .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Google Research, Mountain View, CA, USA

    Golnaz Ghiasi

  3. New York University, New York, USA

    Yi Fang

  4. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

  5. Microsoft Research, Bei**g, China

    Yue Dong

  6. The University of Oklahoma, Norman, OK, USA

    Chris Weaver

  7. University of Maryland, Collage Park, MD, USA

    Zhicheng Leo

  8. University of Central Florida, Orlando, FL, USA

    Joseph J. LaViola Jr.

  9. InnerOptic Technology, Hillsborough, NC, USA

    Luv Kohli

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Zhao, K., Peng, L., Ding, N., Yao, G., Tang, P., Wang, S. (2023). Deep Representation Learning for License Plate Recognition in Low Quality Video Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14362. Springer, Cham. https://doi.org/10.1007/978-3-031-47966-3_16

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  • DOI: https://doi.org/10.1007/978-3-031-47966-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47965-6

  • Online ISBN: 978-3-031-47966-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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