A Quadtree Based Vehicules Recognition System

Abstract − This paper presents a Quadtree based Vehicles Recognition System (SIREVIA), which uses images captured by a digital camera for identifying the vehicle’s license plate. A methodology to identify the license plate in the captured image using digital image processing techniques and artificial intelligent techniques is presented. The plate location algorithm is based on the detection of high contrast areas along the image lines. The character recognition is preformed using a quadtree based object detection algorithm and an automatic classifier using decision trees trained for identifying all the alphabet letters and numbers. A prototype of an access control for a car parking is also presented. This application uses a database witch keeps the information about authorized cars, users and different access levels to a specific place. Key-Words : Licence Plate Recognition; tonal variation; Segmentation; OCR; Quadtree; 1. Introduction The development of automated applications is becoming more and more a request in modern societies, due to the increasing needs for efficiency and fast decisions. The Vehicles Recognition System described in this paper is one of such applications. The use of image processing techniques and artificial intelligence techniques for fast and automatic recognition of vehicles license plates is a result of nowadays technological development and innovation. Some common applications for these systems are access control to restricted car parks and zones, traffic and average speed control and tolling [1] [2]. The absence of any devices installed in the vehicle, such as magnetic cards or transponders, and the capacity to work autonomously without the need of the driver's involvement makes the system management very flexible, allowing upgrades in the behavior without the need for major changes in the global system's architecture. A major example in the area of License Plates' Recognition is Greater London Council’s effort to reduce traffic inside the city [1]. This initiative is the most ambitious and sophisticated attempt that was ever done to reduce traffic problems in a city. According to Transport For London [2], 203 computer-linked camera sets have been set up to photograph the license plates of all vehicles entering the city center, with the intent of automatically detecting, recognizing and punishing the infringer vehicles, with a 90% accuracy rate. Since this is a new area of development, quantitative results for comparison are scarce. In this paper we propose a methodology for automatic detection and recognition of license plates, from static front car images, for controlling the access to restricted car parks. In order to automate this procedure, it is required to correlate computer science techniques and electronics to create a system capable of extracting data from the physical information; a camera for taking the image, a position sensor for detecting the vehicle's presence and processing it using image analysis software. The proposed methodology focuses on three main steps: the tracking of the license plate in the captured image, detection of objects that are within the plate rectangle and in the color range of the plate’s characters, and finally the character recognition. The execution of these steps results in the use of the car plate number in distinct applications. In early stages of the tracking algorithm conception, some studies were carried through, in order to locate the license plate based on the location of the blue field that delimits European plates. However, the different illumination conditions and plate conservation resulted in a high color variance that on the training set, making this a very faulty algorithm. Therefore, a more efficient technique, based on tonal variation analysis, presented in [3] and [4] was used. The object detection algorithm used required an automatic image segmentation associated with the