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Journal Article Temporal Matching Prior Network for Vehicle License Plate Detection and Recognition in Videos
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Authors
Seok Bong Yoo, Mikyong Han
Issue Date
2020-06
Citation
ETRI Journal, v.42, no.3, pp.411-419
ISSN
1225-6463
Publisher
한국전자통신연구원 (ETRI)
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.4218/etrij.2019-0245
Project Code
19MH1500, Development and Demonstration of Smart City Service over 5G Network, Han Mi Kyong
Abstract
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.
KSP Keywords
Bidirectional matching, Data Augmentation, Detection and Recognition, Image rotation, Intelligent Transport Systems(ITS), Layer structure, Network training, Real-world, Recognition Accuracy, Still image, Temporal matching
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
Type 4: