Abstract
In this paper, a new method for license plate detection based on AdaBoost is proposed. In the proposed method, auto-correlation feature, which is ignored by previous learning-based method, is introduced to feature pool. Since that there are two types of Chinese license plate, one type is deeper-background-lighter-character and the other is lighter-background-deeper-character, training a detector cannot convergent. To avoid this problem, two detectors are designed in the proposed method. Experimental results show the superiority of proposed method.
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Tan, H., Deng, Y., Chen, H. (2008). Extracting Auto-Correlation Feature for License Plate Detection Based on AdaBoost. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_10
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DOI: https://doi.org/10.1007/978-3-540-88906-9_10
Publisher Name: Springer, Berlin, Heidelberg
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