27 May 2017 License plate detection in an open environment by density-based boundary clustering
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Abstract
Due to the variation of background, illumination, and view point, license plate detection in an open environment is challenging. We propose a detection method by boundary clustering. To start with, a boundary map is obtained through Canny edge detector and removal of unwanted horizontal background edges. Second, boundaries are classified into different clusters by a density-based approach. In the approach, the density of each boundary is defined by the total gradient intensity of its neighboring and reachable boundaries. Also, the cluster centers and the number of them are determined automatically according to a minimum-distance principle. At last, a set of horizontal candidate regions with accurately located borders are extracted for classification. The classifier is trained on the histogram of oriented gradient feature by a linear support vector machine model. Experiments on three public datasets including images captured under different scenarios demonstrate that the proposed method outperforms several state-of-the-art methods in detection accuracy and its performance in efficiency is also comparable.
© 2017 SPIE and IS&T
Jiangmin Tian, Jiangmin Tian, Guoyou Wang, Guoyou Wang, Jianguo Liu, Jianguo Liu, Yuanchun Xia, Yuanchun Xia, } "License plate detection in an open environment by density-based boundary clustering," Journal of Electronic Imaging 26(3), 033017 (27 May 2017). https://doi.org/10.1117/1.JEI.26.3.033017 . Submission: Received: 5 January 2017; Accepted: 11 April 2017
Received: 5 January 2017; Accepted: 11 April 2017; Published: 27 May 2017
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