طراحی و پیاده‌سازی سامانۀ بی‌درنگ آشکارسازی و شناسایی پلاک خودرو در تصاویر ویدئویی

An automatic Number Plate Recognition (ANPR) is a popular topic in the field of image processing and is considered from different aspects, since early 90s. There are many challenges in this field, including; fast moving vehicles, different viewing angles and different distances from camera, complex and unpredictable backgrounds, poor quality images, existence of multiple plates in the scene, variable lighting conditions throughout the day, and so on. ANPR systems have many applications in today’s traffic monitoring and toll-gate systems. In this paper, a real-time algorithm is designed and implemented for simultaneous detection and recognition of multiple number plates in video sequences. Already some papers on plate localization and recognition in still? images have been existed , however, they do not consider real time processing. While for the related applications, real-time detection and recognition of multiple plates on the scene is very important. Unlike methods with high computational complexity, we apply simple and effective techniques for being real-time. At first, background is modeled using Gaussian Mixture Model (GMM) and moving objects are determined. Then, plate candidate regions are found by vertical edge detection and horizontal projection. After that, license plates are localized and extracted by morphological operations and connected components analysis. When plates were are detected, their characters are separated with another algorithm. Finally a neural network is applied for character recognition. This system is implemented in C++ using OpenCV library. The average localization time per frame is 25 ms and total processing time, including localization and recognition, is 40 ms that can be used in real-time applications. The proposed method is evaluated on videos from highway cameras and the detection rate of 98.79% and recognition rate of 97.83% is obtained. Our real-time system can also recognize multiple plates of different types in each frame. Experimental results show that our method have higher speed and better recognition rate than previous works therefore it is suitable for real-time applications.