A vision-based machine learning method for barrier access control using vehicle license plate authentication

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications

DESIGN AND PROTOTYPE IMPLEMENTATION OF AUTOMATIC PARKING SYSTEM

Parking system became a necessity for public places especially in big cities. For reasons of efficiency of human resources, the automatic parking system was developed. Automatic parking system is a parking system without any human intervention as a system operator. To create a fully automated system, requires a mechanism automatically recording the vehicle's identity and security mechanisms to prevent acts of theft. Vehicle plate number recognition is a method of image processing to recognize license plate number and vehicle color recognition as an additional method, to provide an additional vehicle identity. The use of two recognition methods to make the system work automatically. To add security features to the system multifactor authentication is applied. This paper presents a study, design and prototyping of automatic car park system. Where plate number recognition and color recognition are adopted to make full automatic system. The use of technologies such as RFID and microcontroller is needed in the future to implement real systems that is completely automated. The design of this system is expected to provide the efficiency of human resources. Keywords: automatic parking system, prototype, recognition, plate numbe

Automated License Plate Recognition using Existing University Infrastructure and Different Camera Angles

Number or license plate recognition has become an essential technology for traffic and security applications. Providing access control at any organization or academic institution improves the level of security. However, providing security personnel to manually control the access of vehicles at an academic institution is costly, time-consuming, and to a limited extent, error prone. This study investigated the use of an automated vehicle tracking system, incorporating experimental computer vision techniques for license plate recognition that runs in real-time to provide access control for vehicles and provide increased security for an academic institution. A vehicle monitoring framework was designed by using various technologies and experimenting with different camera angles. In addition, the effect of environmental changes on the accuracy of the optical character recognition application was assessed. The Design Science Research methodology was followed to develop the vehicle monitoring framework artifact. Image enhancement algorithms were tested, and the most viable options were evaluated and implemented. Optimal operating criteria that were established for the vehicle monitoring framework achieved a 96% success rate. The results indicate that a cost-effective solution could be provided by using an existing camera infrastructure at an academic institution and suitable license plate recognition software technologies, algorithms, and different camera angles

An End-to-End Automated License Plate Recognition System Using YOLO Based Vehicle and License Plate Detection with Vehicle Classification

An accurate and robust Automatic License Plate Recognition (ALPR) method proves surprising versatility in an Intelligent Transportation and Surveillance (ITS) system. However, most of the existing approaches often use prior knowledge or fixed pre-and-post processing rules and are thus limited by poor generalization in complex real-life conditions. In this paper, we leverage a YOLO-based end-to-end generic ALPR pipeline for vehicle detection (VD), license plate (LP) detection and recognition without exploiting prior knowledge or additional steps in inference. We assess the whole ALPR pipeline, starting from vehicle detection to the LP recognition stage, including a vehicle classifier for emergency vehicles and heavy trucks. We used YOLO v2 in the initial stage of the pipeline and remaining stages are based on the state-of-the-art YOLO v4 detector with various data augmentation and generation techniques to obtain LP recognition accuracy on par with current proposed methods. To evaluate our approach, we used five public datasets from different regions, and we achieved an average recognition accuracy of 90.3% while maintaining an acceptable frames per second (FPS) on a low-end GPU

Deteksi Posisi dan Pengenalan Plat Nomor Kendaraan Menggunakan Single Shot Detector dan Recurrent Neural Network pada Data Video

Setiap kendaraan mempunyai identitasnya masing-masing, dengan kata lain, plat nomor kendaraan. Identitas ini sering kali dipergunakan dalam sistem pengolahan parkir, pengamanan, dan sebagainya. Untuk membuat sistem ini andal, diperlukan adanya pengembangan sistem otomatis yang dapat mendeteksi dan mengidentifikasi plat nomor kendaraan. Sistem ini telah banyak dikenal sebagai License Plate Recognition (LPR). LPR menggunakan konsep deteksi posisi (lokalisasi) dan segmentasi untuk identifikasi plat kendaraan pada suatu citra. Hasil plat yang telah terdeteksi nantinya akan dikenali sebagai karakter-karakter yang merepresentasikan identitas kendaraan. Dalam tugas akhir ini, akan dilakukan pengembangan LPR yang memungkinkan membaca masukan dari data video yang mengacu pada real-time processing. Dengan menggunakan metode lokalisasi Single Shot Detector, segmentasi Binary Image diikuti dengan Connected Component Labelling, dan pengenalan karakter Recurrent Neural Network, hasil dari penelitian ini menunjukkan akurasi sebesar 91.48% untuk lokalisasi plat, 82.69% untuk segmentasi, dan 94.94% untuk pengenalan karakter. ================================================================================================================================== Each vehicle has its own identity, in other words, the vehicle number plate. This identity is often used in parking processing, security, and so on. To make this system, it is necessary to develop an automated system that can be used and supported by vehicle number plates. This system is known as License Plate Recognition (LPR). LPR uses the concept of position detection (localization) and segmentation to determine vehicle plates in images. The results of the verified plate will be recognized as characters that represent the vehicle's identity. In this undergraduate thesis, the development of LPR will be made which allows reading input in the form of video data that refers to real-time processing. By using the Single Shot Detector localization method, Binary Image segmentation followed by Connected Component Labeling, and Recurrent Neural Network character recognition, the results of this study show an accuracy of 91.48% for plate localization, 82.69% for segmentation, and 94.94% for character recognition

Prevention of Unauthorized Transport of Ore in Opencast Mines Using Automatic Number Plate Recognition

Security in mining is a primary concern, which mainly affects the production cost. An efficiently detecting and deterring theft will maximize the profitability of any mining organization. Many illegal transportation cases were registered in spite of rules imposed by central and state governments under Section 23 (c) of MMDR Act 1957. Use of an automated checkpoint gate based on license plate recognition and biometric fingerprint system for vehicle tracking enhances the security in mines. The method was tested on the number plates with various considerations like clean number plates, clean fingerprints, dusty and faded number plates, dusty fingerprints, and number plates captured by varying distance. By considering all the above conditions the pictures were processed by ANPR and bio-metric fingerprint modules. Vehicle license number plate was captured using a digital camera and the captured RGB image was converted to grayscale image. Thresholding was done to remove unwanted areas from the grayscale image. The characters of the number plate were segmented using Gabor filter. A track-sector matrix was generated by considering the number of pixels in each region and was matched with existing template to identify the character. The fingerprint scans the finger and matches with the template created at the time of fingerprint registration at the machine. The micro-controller accepted the processed output in binary form from ANPR and bio-metric fingerprint system. The micro-controller processed the binary output and the checkpoint gate was closed/open based on the output provided by the microcontroller to motor driver

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