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Advances in Multimedia
Volume 2017 (2017), Article ID 7587841, 9 pages
https://doi.org/10.1155/2017/7587841
Research Article

Vehicle Plate Detection in Car Black Box Video

Department of Embedded Systems Engineering, Incheon National University, Incheon, Republic of Korea

Correspondence should be addressed to Kyungkoo Jun; rk.ca.uni@nujk

Received 26 May 2017; Revised 28 September 2017; Accepted 8 November 2017; Published 28 November 2017

Academic Editor: Constantine Kotropoulos

Copyright © 2017 Dongjin Park and Kyungkoo Jun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Internet services that share vehicle black box videos need a way to obfuscate license plates in uploaded videos because of privacy issues. Thus, plate detection is one of the critical functions that such services rely on. Even though various types of detection methods are available, they are not suitable for black box videos because no assumption about size, number of plates, and lighting conditions can be made. We propose a method to detect Korean vehicle plates from black box videos. It works in two stages: the first stage aims to locate a set of candidate plate regions and the second stage identifies only actual plates from candidates by using a support vector machine classifier. The first stage consists of five sequential substeps. At first, it produces candidate regions by combining single character areas and then eliminates candidate regions that fail to meet plate conditions through the remaining substeps. For the second stage, we propose a feature vector that captures the characteristics of plates in texture and color. For performance evaluation, we compiled our dataset which contains 2,627 positive and negative images. The evaluation results show that the proposed method improves accuracy and sensitivity by at least 5% and is 30 times faster compared with an existing method.