License Plate Extraction for Moving Vehicles

Y. Cheon; C. Lee

doi:10.1109/IISA.2019.8900778

License Plate Extraction for Moving Vehicles

Y. Cheon, C. Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, a license plate extraction algorithm is proposed, which can be used in moving vehicles. First, the Haar cascade classifier was used to find candidate regions. Then, a DoG filter was used to detect the edges and connected component labeling was applied to obtain the candidate blocks. The license plate color characteristics were used to eliminate irrelevant blocks using histogram comparison and color quantization. The Bhattacharyya distance and the correlation metric were used to compare the histograms. Experiments with real data showed good performance. The dataset consists of various road and weather conditions including expressway, downtown, sunny days and rainy days. For our dataset, the recall was 0.72, the precision was 0.88 and the F-score was 0.79. For the Caltech dataset, the recall was 0.86, the precision was 0.96 and the F-score was 0.91.

Original language	English
Title of host publication	10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728149592
DOIs	https://doi.org/10.1109/IISA.2019.8900778
Publication status	Published - 2019 Jul
Event	10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 - Patras, Greece Duration: 2019 Jul 15 → 2019 Jul 17

Publication series

Name	10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

Conference

Conference	10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
Country/Territory	Greece
City	Patras
Period	19/7/15 → 19/7/17

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07050345).

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Artificial Intelligence
Computer Vision and Pattern Recognition
Information Systems
Information Systems and Management
Media Technology
Education

Access to Document

10.1109/IISA.2019.8900778

Cite this

Cheon, Y., & Lee, C. (2019). License Plate Extraction for Moving Vehicles. In 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 [8900778] (10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IISA.2019.8900778

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title = "License Plate Extraction for Moving Vehicles",

abstract = "In this paper, a license plate extraction algorithm is proposed, which can be used in moving vehicles. First, the Haar cascade classifier was used to find candidate regions. Then, a DoG filter was used to detect the edges and connected component labeling was applied to obtain the candidate blocks. The license plate color characteristics were used to eliminate irrelevant blocks using histogram comparison and color quantization. The Bhattacharyya distance and the correlation metric were used to compare the histograms. Experiments with real data showed good performance. The dataset consists of various road and weather conditions including expressway, downtown, sunny days and rainy days. For our dataset, the recall was 0.72, the precision was 0.88 and the F-score was 0.79. For the Caltech dataset, the recall was 0.86, the precision was 0.96 and the F-score was 0.91.",

author = "Y. Cheon and C. Lee",

note = "Funding Information: ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07050345). Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 ; Conference date: 15-07-2019 Through 17-07-2019",

year = "2019",

month = jul,

doi = "10.1109/IISA.2019.8900778",

language = "English",

series = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019",

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}

Cheon, Y & Lee, C 2019, License Plate Extraction for Moving Vehicles. in 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019., 8900778, 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019, Institute of Electrical and Electronics Engineers Inc., 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019, Patras, Greece, 19/7/15. https://doi.org/10.1109/IISA.2019.8900778

License Plate Extraction for Moving Vehicles. / Cheon, Y.; Lee, C.

10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8900778 (10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - License Plate Extraction for Moving Vehicles

AU - Cheon, Y.

AU - Lee, C.

N1 - Funding Information: ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07050345). Publisher Copyright: © 2019 IEEE.

PY - 2019/7

Y1 - 2019/7

N2 - In this paper, a license plate extraction algorithm is proposed, which can be used in moving vehicles. First, the Haar cascade classifier was used to find candidate regions. Then, a DoG filter was used to detect the edges and connected component labeling was applied to obtain the candidate blocks. The license plate color characteristics were used to eliminate irrelevant blocks using histogram comparison and color quantization. The Bhattacharyya distance and the correlation metric were used to compare the histograms. Experiments with real data showed good performance. The dataset consists of various road and weather conditions including expressway, downtown, sunny days and rainy days. For our dataset, the recall was 0.72, the precision was 0.88 and the F-score was 0.79. For the Caltech dataset, the recall was 0.86, the precision was 0.96 and the F-score was 0.91.

AB - In this paper, a license plate extraction algorithm is proposed, which can be used in moving vehicles. First, the Haar cascade classifier was used to find candidate regions. Then, a DoG filter was used to detect the edges and connected component labeling was applied to obtain the candidate blocks. The license plate color characteristics were used to eliminate irrelevant blocks using histogram comparison and color quantization. The Bhattacharyya distance and the correlation metric were used to compare the histograms. Experiments with real data showed good performance. The dataset consists of various road and weather conditions including expressway, downtown, sunny days and rainy days. For our dataset, the recall was 0.72, the precision was 0.88 and the F-score was 0.79. For the Caltech dataset, the recall was 0.86, the precision was 0.96 and the F-score was 0.91.

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M3 - Conference contribution

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BT - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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License Plate Extraction for Moving Vehicles

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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