Region-based license plate detection

Automatic license plate recognition (ALPR) is one of the most important aspects of applying computer techniques towards intelligent transportation systems. In order to recognize a license plate efficiently, however, the location of the license plate, in most cases, must be detected in the first place. Due to this reason, detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of an ALPR system, which greatly affects the recognition rate and speed of the whole system. In this paper, a region-based license plate detection method is proposed. In this method, firstly, mean shift is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. Unlike other existing license plate detection methods, the proposed method focuses on regions, which demonstrates to be more robust to interference characters and more accurate when compared with other methods. © 2006 Elsevier Ltd. All rights reserved

An Efficient Traffic Control System and License Plate Detection Using Image Processing

Automatic license plate recognition is extracted from license plate of the vehicle. It is taken as an image or a continuous image taken in sequence. The extracted information can be with or without a database in many applications like electronic payment systems and freeway and arterial monitoring devices for traffic surveillance. ALPR employs CC camera, advanced camera or black and white, color camera to capture the image. ALPR is fruitful if the captured images are of good quality. ALPR is a real time application that processes the images of license plates in various conditions like dark or bright times in a day. A general technique should be identified to process images in many different countries or states. We should know that the license plate generally consists of various colors, languages, fonts and others have images in the background. Also, these plates are obstructed by mud, light, some accessories especially on a car. Here, we discuss about methods for ALPR. We classify ALPR based on the features they are used in each method and knowing their advantages, disadvantages, recognition accuracy and processing speed. Managing the timing in traffic controlling by calculating the density of an image

OCR Applied for Identification of Vehicles with Irregular Documentation Using IoT

Given the lack of investments in surveillance in remote places, this paper presents a prototype that identifies vehicles in irregular conditions, notifying a group of people, such as a network of neighbors, through a low-cost embedded system based on the Internet of things (IoT). The developed prototype allows the visualization of the location, date and time of the event, and vehicle information such as license plate, make, model, color, city, state, passenger capacity and restrictions. It also offers a responsive interface in two languages: Portuguese and English. The proposed device addresses technical concepts pertinent to image processing such as binarization, analysis of possible characters on the plate, plate border location, perspective transformation, character segmentation, optical character recognition (OCR) and post-processing. The embedded system is based on a Raspberry having support to GPS, solar panels, communication via 3G modem, wi-fi, camera and motion sensors. Tests were performed regarding the vehicle’s positioning and the percentage of assertiveness in image processing, where the vehicles are at different angles, speeds and distances. The prototype can be a viable alternative because the results were satisfactory concerning the recognition of the license plates, mobility and autonomy

Fuzzy and Neural Network Based License- Plate Localization and Recognition

ABSTRACT: This paper presents the extraction of vehicle license plate information from a sequence of various images. ALPR is used in the presence or absence of a database in various applications such as, toll payment, etc. The proposed technique implements the CAN image by using a camera and to extract the license plate from the image based on various characteristics, such as the boundary, the color, or the existence of the characters. Thus it is not suitable for location of variable number plate. Finally to recognize the extracted characters by template matching by using neural networks and fuzzy classifiers. KEYWORDS: vertical edge detection algorithm (VEDA), scale-invariant feature transform(SIFT), dynamic programming(DP). Observing the notion of cars and do not belong in a parking garage. I.INTRODUCTION Automated By eliminating the parking as expedited which is the need for human confirmation of parking passes. The organization of the paper is mentioned as follows. In section II, a detailed review of ALPR Technique. Section III , illustrates the various algorithms used in ALPR Technique. Section IV features out the proposed method. Section V and VI narrates the experimental and simulation results and Section VII concludes the paper and defines the future works. ISSN (Print Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8144 II. ALPR TECHNIQUE ALPR is known by several other names, including Automatic Number Plate Recognition (ANPR), Automatic Vehicle Identification (AVI), Car Plate Recognition (CPR), License Plate Recognition (LPR), and Lecture Automatique de Plaquesd' Immatriculation (LAPI). Other name of ALPR are followed as car plate recognition, automatic vehicle identification, and optical character recognition for cars Optical character recognition, usually abbreviated as OCR, is conversion by electronic and mechanical of scanned images which may be of writing by hand, or printed characters and numbers into machine-encoded format of characters and numbers. It is widely used as a form of data entry from some sort of original paper data source, whether documents, sales receipts, mail, or any number of printed records It is a common method of digitizing printed characters and numbers so that they can be electronically searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, textto-speech and text mining[6]- Early versions needed to be programmed with images of each and every texts at a time implied on a single font. "Intelligent" systems with a greater degree of recognition accuracy for most fonts are now common III. ALGORITHMS USED IN ALPR TECHNIQUE The ALPR system comprising of four levels from the input image results during the extraction of a license plate number. The first level is the location of an image of a car by utilizing a camera. Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8145 The second level is the extraction of a license plate from the given input image based on the following features as boundary, existence of the characters and the color. The third level is the character extraction and segmentation of license plate. The final level is the recognization of character extraction by the matching of templates such as fuzzy classifiers. This paper illustrates the procedural types of various algorithms, They are as follows: A. Block converter Block converter is the converter ,which is used to convert the image to sub blocks from the sub block , use selected box only for segmenting the number plate. The block-based method is also presented in which the blocks with greater magnitude edges are visualized as areas of license plate. Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8146 B. Otsu The number plate consist of fore ground information and back ground data. In this algorithm the necessity of numbers only utilize the threshold segmentation. C. State-of-Art It is the feature based process. Training data convert to feature points. Feature point only depends upon the shape of the training image. Then it is compared to the number plate feature points Backgrounds of license plate and characters has varying colors, possessing opposite binary values in the binary image. Horizontal projection of used to extract the characters along with noise removal and analyzing the simplicity. To convert grayscale im-age into a binary image by using a threshold operation. There are basically two types of threshold operation

Sistem Verifikasi Nomor Kendaraan Bermotor Dengan Database Menggunakan Pengolahan Citra Digital Pada Sistem Keluaran Parkir

ABSTRAKSI: Keamanan suatu sistem parkir merupakan hal yang sangat diperlukan. Seperti halnya tempat parkir yang ada di Institut Teknologi Telkom dan Politeknik Telkom, dimana sistem parkir yang digunakan hanya mengandalkan kartu parkir saja tanpa memperhatikan identitas kendaraan yang dibawa keluar.Pada tugas akhir ini, didesain sistem verifikasi nomor kendaraan bermotor yang tercantum pada database pada masing – masing pemegang user tag dengan nomor kendaraan yang tercapture oleh webcam. Digunakan dua buah webcam pada penelitian, webcam pertama mengenali warna pada color code matrix, pada proses ini digunakan metode deteksi berdasarkan evaluasi pada komponen YCbCr dari warna color code matrix. Kemudian dibuat sensor warna dan sensor background supaya penempatan posisi color code matrix tepat sehingga dapat terbaca dengan benar.Setiap sensor warna akan mensampling komponen YCbCr untuk mengenali tiap warnanya.Webcam kedua mengcapture kendaraan dari pemegang user tag, dan hasil capture akan diproses untuk didapatkan posisi plat nomor kendaraan kemudian dikenali tiap karakternya. Proses pencarian posisi plat nomor kendaraan menggunakan sistem pelabelan, penghitungan luas tiap label, konvolusi dan meremove luas label yang lebih dari threshold yang telah ditentukan berdasarkan luas dari plat nomor kendaraan. Sedangkan untuk pengenalan karakter menggunakan K-Nearest neighbor (KNN).Kehandalan sistem, diperoleh dengan melakukan uji coba outdoor dalam 3 skenario, antara lain: uji coba pencarian plat nomor kendaraan bermotor pada kondisi pagi, siang, dan sore hari. Pada proses cropping, dihasilkan tingkat akurasi sebesar 96.67% untuk sepeda motor sedangkan untuk mobil diperoleh tingkat akurasi 100%. Selanjutnya pada pengenalan karakter, diperoleh tingkat akurasi sebesar 67.46% untuk sepeda motor dengan waktu yang dibutuhkan 9.02 detik dan 84.76% untuk mobil dengan waktu 9.85detik.Kata Kunci : color code matrix, sistem parkir, database, K-Nearest neighbor, plat nomorABSTRACT: Security parking system is very necessary. Like the existing parking lot at the Institut Teknologi Telkom and Politeknik Telkom, in which the parking system that is used only rely on just a parking card, regardless of the identity of vehicles that were taken out.In this thesis, the verification system is designed vehicle numbers listed in the database on each - each holder of user tags with the number of vehicles that captured by webcam. Used by the two webcam on research, the first webcam to recognize the color on the color code matrix, in this process is used detection method based on evaluation of the color components of YCbCr color code matrix. Then made the background color sensor and sensor placement so that precise positioning matrix color code so that it can be read with a color sensor correctly. Each sensor will sampling colors to identify each component YCbCr color. Second webcam capture of the holder of the vehicle user tags, and capture the results will be processed to obtain the position plate vehicle numbers and then identified each character. The process of finding position of vehicle number plates using a labeling system, the counting area of each label, convolution and the label widely remove a more than predetermined threshold based on the area of vehicle number plates. While for character recognition using K-Nearest neighbor (KNN).System reliability, obtained by conducting outdoor trials in the three scenarios, among others: the search pilot license plates of vehicles on the condition of the morning, afternoon, and evening. In the process of cropping, resulting level of accuracy of 96.67% for motorcycles and for cars acquired 100% accuracy. Furthermore, the introduction of characters, obtained 67.46% accuracy for for motorcycles with a time of 9.02 seconds and required 84.76% for cars with a time of 9.85 seconds.Keyword: color matrix code, parking system, database, K-Nearest neighbor, license plat

Road Vehicle Monitoring System Based on Intelligent Visual Internet of Things

In recent years, with the rapid development of video surveillance infrastructure, more and more intelligent surveillance systems have employed computer vision and pattern recognition techniques. In this paper, we present a novel intelligent surveillance system used for the management of road vehicles based on Intelligent Visual Internet of Things (IVIoT). The system has the ability to extract the vehicle visual tags on the urban roads; in other words, it can label any vehicle by means of computer vision and therefore can easily recognize vehicles with visual tags. The nodes designed in the system can be installed not only on the urban roads for providing basic information but also on the mobile sensing vehicles for providing mobility support and improving sensing coverage. Visual tags mentioned in this paper consist of license plate number, vehicle color, and vehicle type and have several additional properties, such as passing spot and passing moment. Moreover, we present a fast and efficient image haze removal method to deal with haze weather condition. The experiment results show that the designed road vehicle monitoring system achieves an average real-time tracking accuracy of 85.80% under different conditions

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear

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