Real-Time License Plate Recognition and Vehicle Tracking System Based on Deep Learning
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- Guan-Wen Chen
- National Yang Ming Chiao Tung University
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- Chun-Min Yang
- National Yang Ming Chiao Tung University
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- Tsi-Ui _Ik
- National Yang Ming Chiao Tung University
抄録
Traditional license plate recognition technology mostly uses traditional image processing methods to find out the characteristics of the license plate, and then crop and recognize the characters. The process needs to be modified due to the different environments, scenes and conditions. In recent years, many studies have implemented license plate and character recognition by using deep learning algorithms. Although it has a good recognition accuracy, the calculation speed still cannot reach the level of real-time recognition. This research proposes a real-time license plate recognition system based on YOLOv3, which uses deep learning model to realize the vehicle license plate recognition, lane identification and vehicle trajectory tracking. In this study, a web-based platform is established to present the result of license plate recognition and trajectory, and the streaming roadside video in the campus. In the platform, license plates of driving vehicles can be identified in real-time, and the user can search and track specific vehicle intuitively. In the experiment, the average accuracy of the system performs 84.3% in real-time license plate recognition, and 100% in lane identification. The system can process in 40 FPS, which can meet the level of real-time system. In the future, the system can cooperate with traffic access control in campus or community to improve the efficiency of traffic control.
収録刊行物
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- IEICE Proceeding Series
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IEICE Proceeding Series 67 378-381, 2021-09-08
The Institute of Electronics, Information and Communication Engineers
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詳細情報 詳細情報について
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- CRID
- 1390289796573146880
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- NII論文ID
- 230000012813
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- ISSN
- 21885079
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可