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http://dx.doi.org/10.9717/kmms.2017.20.8.1166

A Car Plate Area Detection System Using Deep Convolution Neural Network  

Jeong, Yunju (School of Computer Science & Eng., Graduate School, Kyungpook National University)
Ansari, Israfil (Dept. of Computer Eng. Andong National University)
Shim, Jaechang (Dept. of Computer Eng. Andong National University)
Lee, Jeonghwan (Dept. of Electronic Eng. Andong National University)
Publication Information
Journal of Korea Multimedia Society / v.20, no.8, 2017 , pp. 1166-1174 More about this Journal
Abstract
In general, the detection of the vehicle license plate is a previous step of license plate recognition and has been actively studied for several decades. In this paper, we propose an algorithm to detect a license plate area of a moving vehicle from a video captured by a fixed camera installed on the road using the Convolution Neural Network (CNN) technology. First, license plate images and non-license plate images are applied to a previously learned CNN model (AlexNet) to extract and classify features. Then, after detecting the moving vehicle in the video, CNN detects the license plate area by comparing the features of the license plate region with the features of the license plate area. Experimental result shows relatively good performance in various environments such as incomplete lighting, noise due to rain, and low resolution. In addition, to protect personal information this proposed system can also be used independently to detect the license plate area and hide that area to secure the public's personal information.
Keywords
CNN; License Plate Detection; Optical Flow; Difference of Gaussian;
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Times Cited By KSCI : 3  (Citation Analysis)
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