Desenvolupament del sistema de control d’accés al nucli antic de Sant Llorenç de Morunys

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Sergio Escalera Guerrero i Marc PomarThis project includes the installation of a vehicle access control system in the old town of Sant Llorenç de Morunys : - Portal de la Capella - Portal del Puit. The purpose of the project is to minimize the social and landscape impact caused by this motorized traffic . The installation is formed by a system of IP cameras to capture and transmit the different accesses, which will be analyzed and stored by a registration plate reading system and resulting data management. The reading system is based on a computer vision system known as ALPR or ANPR (Automatic Number/License Plate Recognition). This is a method that uses OCR (Optical Character Recognition) in image recognition to read license plates and extract their alphanumeric values and detect its existence and position. On the other hand, a database and web interface are created for the management and interaction of the information generated. The features offered are the following: add authorized vehicles, add periods of days when access is restricted, handle license plate reading errors, generate PDF to the sanction process and lastly show access statistics. Finally, readings have been validated in different tests conducted throughout the project and an usability analysis has also been made to adapt the design to the client’s needs

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset, called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201

License plate localization based on statistical measures of license plate features

— License plate localization is considered as the most important part of license plate recognition system. The high accuracy rate of license plate recognition is depended on the ability of license plate detection. This paper presents a novel method for license plate localization bases on license plate features. This proposed method consists of two main processes. First, candidate regions extraction step, Sobel operator is applied to obtain vertical edges and then potential candidate regions are extracted by deploying mathematical morphology operations [5]. Last, license plate verification step, this step employs the standard deviation of license plate features to confirm license plate position. The experimental results show that the proposed method can achieve high quality license plate localization results with high accuracy rate of 98.26 %