An algorithm combined with color differential models for license-plate location

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• 作者：Yuanmei Tian^aAuthor Vitae ; Juan Song^bAuthor Vitae ; Xiangdong Zhang^aAuthor Vitae ; Peiyi Shen^b ; ^{pyshen@xidian.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Liang Zhang^bAuthor Vitae ; Weibin Gong^cAuthor Vitae ; Wei Wei^dAuthor Vitae ; Guangming Zhu^bAuthor Vitae
• 关键词：License plate location ; Color differential model ; Adaboost algorithm
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 November 2016
• 年：2016
• 卷：212
• 期：Complete
• 页码：22-35
• 全文大小：7557 K

文摘

Vehicle license plate positioning is the first and most important step in license plate recognition systems. Existing license plate location algorithms are sensitive to light conditions and prone to be influenced by the background interference. To solve these problems, this paper presents an adaboost algorithm combined with color differential model. The proposed algorithm is composed of a coarse location step and a precise location step. In the coarse location step, a binary image is obtained to select the candidate plate regions using the color differential model. Then in the precise location step, the features obtained above together with other features are used by the adaboost algorithm to train the classifiers and precisely locate the license plates. The experimental results show that the proposed algorithm is more robust to the light conditions and background interference. In particular, during nighttime the precision rate can attain above 95.0%.