6,809 research outputs found

    Image Processing in Road Traffic Analysis

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    The article presents an application of computer vision methods to traffic flow monitoring and road traffic analysis. The application is utilizing image-processing and pattern recognition methods designed and modified to the needs and constrains of road traffic analysis. These methods combined together gives functional capabilities of the system to monitor the road, to initiate automated vehicle tracking, to measure the speed, and to recognize number plates of a car. Software developed was applied in and approved with video monitoring system, based on standard CCTV cameras connected to wide area network computers

    Automatic vehicle identfication for Argentinean license plates using intelligent template matching

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    The problem of automatic number plate recognition (ANPR) has been studied from different aspects since the early 90s. Efficient approaches have been recently developed, particularly based on the features of the license plate representation used in different countries. This paper focuses on a novel approach to solving the ANPR problem for Argentinean license plates, called Intelligent Template Matching (ITM). We compare the performance obtained with other competitive approaches to robust pattern recognition (such as artificial neural networks), showing the advantages both in classification accuracy and training time. The approach can also be easily extended to other license plate representation systems different from the one used in Argentina.Fil: Gazcón, Nicolás Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Castro, Silvia Mabel. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Visualización yComputación Gráfica; Argentin

    Automatic Vehicle Detection and Identification using Visual Features

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    In recent decades, a vehicle has become the most popular transportation mechanism in the world. High accuracy and success rate are key factors in automatic vehicle detection and identification. As the most important label on vehicles, the license plate serves as a mean of public identification for them. However, it can be stolen and affixed to different vehicles by criminals to conceal their identities. Furthermore, in some cases, the plate numbers can be the same for two vehicles coming from different countries. In this thesis, we propose a new vehicle identification system that provides high degree of accuracy and success rates. The proposed system consists of four stages: license plate detection, license plate recognition, license plate province detection and vehicle shape detection. In the proposed system, the features are converted into local binary pattern (LBP) and histogram of oriented gradients (HOG) as training dataset. To reach high accuracy in real-time application, a novel method is used to update the system. Meanwhile, via the proposed system, we can store the vehicles features and information in the database. Additionally, with the database, the procedure can automatically detect any discrepancy between license plate and vehicles

    An End-to-End License Plate Localization and Recognition System

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    An end-to-end license plate recognition (LPR) system is proposed. It is composed of pre-processing, detection, segmentation and character recognition to find and recognize plates from camera based still images. The system utilizes connected component (CC) properties to quickly extract the license plate region. A novel two-stage CC filtering is utilized to address both shape and spatial relationship information to produce high precision and recall values for detection. Floating peak and valleys (FPV) of projection profiles are used to cut the license plates into individual characters. A turning function based method is proposed to recognize each character quickly and accurately. It is further accelerated using curvature histogram based support vector machine (SVM). The INFTY dataset is used to train the recognition system. And MediaLab license plate dataset is used for testing. The proposed system achieved 89.45% F-measure for detection and 87.33% accuracy for overall recognition rate which is comparable to current state-of-the-art systems

    Recognition Design of License Plate and Car Type Using Tesseract Ocr and Emgucv

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    The goal of the research is to design and implement software that can recognize license plates and car types from images. The method used for the research is soft computing using library of EmguCV. There are four phases in creating the software, i.e., input image process, pre-processing, training processing and recognition. Firstly, user enters the car image. Then, the program reads and does pre-processing the image from bitmap form into vector. The next process is training process, which is learning phase in order the system to be able recognize an object (in this case license plate and car type), and in the end is the recognition process itself. The result is data about the car types and the license plates that have been entered. Using simulation, this software successfully recognized license plate by 80.223% accurate and car type 75% accurate

    Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates

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    In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate

    System for Detecting Vehicle Features from Low Quality Data

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    The paper presents a system that recognizes the make, colour and type of the vehicle. The classification has been performed using low quality data from real-traffic measurement devices. For detecting vehicles’ specific features three methods have been developed. They employ several image and signal recognition techniques, e.g. Mamdani Fuzzy Inference System for colour recognition or Scale Invariant Features Transform for make identification. The obtained results are very promising, especially because only on-site equipment, not dedicated for such application, has been employed. In case of car type, the proposed system has better performance than commonly used inductive loops. Extensive information about the vehicle can be used in many fields of Intelligent Transport Systems, especially for traffic supervision. 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    Automatic License Plate Recognition System using Histogram Graph Algorithm

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    Character recognition is one of the most important applications of image processing. In last couple of decades, the numbers of vehicles have been increasing day by day. With this increase, it is becoming difficult to keep track of each vehicle for the purpose of law enforcement and traffic management. Thus, we need that type of system, which is capable of providing appropriate solutions to the traffic issues and hence this Automatic License Plate Recognition System is developed using Histogram graph algorithm in MATLAB. In which image is captured from camera and converted into Gray scale image for preprocessing .After conversion, dilation process is applied on image. After dilation horizontal and vertical edge processing has been done and passed these histograms through low pass filter. After filtration, image is segmented and license plate is extracted. For detection each character separately from detected license plate smearing algorithm is used
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