ETRI Journal

  • 제42권3호
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  • Pages.411-419
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  • 2020
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Temporal matching prior network for vehicle license plate detection and recognition in videos

  • Yoo, Seok Bong (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Han, Mikyong (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute)
  • 투고 : 2019.05.05
  • 심사 : 2019.10.28
  • 발행 : 2020.06.08
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초록

In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

키워드

참고문헌

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피인용 문헌

  1. Label-preserving data augmentation for mobile sensor data vol.32, pp.1, 2020, https://doi.org/10.1007/s11045-020-00731-2
  2. CitiusSynapse: A Deep Learning Framework for Embedded Systems vol.11, pp.23, 2020, https://doi.org/10.3390/app112311570