Vehicle License Plate Number Research Articles

IMPLEMENTATION OF IMAGE PROCESSING IN THE RECOGNITION OF OFFICIAL VEHICLE LICENSE PLATES

Vehicle license plates are identifiers used to uniquely identify vehicles. However, to identify vehicle license plates there are several problems encountered, namely the different formats of vehicle license plates that make license plate recognition more complicated, vehicle license plates often contain visually similar combinations of letters and numbers (for example the letter "O" and the number "0" or the letter "I" and the number "1"), . in poor lighting conditions license plates may not be clearly visible. To solve this problem, image recognition, image processing, and pattern recognition technologies can be used. The three technologies can be used to recognize characters on vehicle license plates, but cannot yet be used to recognize the colors contained on vehicle license plates. The purpose of this research is to identify and record vehicle license plate numbers quickly and accurately, monitor the presence of vehicles in a supervised area, assist in managing parking, reduce the need for human interaction in the vehicle identification process, The methods used to recognize motor vehicle plates are edge detection and character segmentation which involves image processing to detect the edges of the vehicle plate, followed by segmentation of individual characters in the plate. Another method used is optical character recognition which involves using an optical sensor to take an image of a vehicle plate, then using character recognition techniques to identify the numbers and letters on the plate. The result of this research is that the motor vehicle number recognition system can work in various lighting conditions and poor weather conditions and can monitor and control vehicles in the parking area. The finding obtained from this research is that no method has been used for color recognition on motor vehicle plates.

An Improved Visual Attention Model for Automated Vehicle License Plate Number Recognition Using Computer Vision

The role of an automatic licensed plate detection system (ALPDS) cannot be over-emphasized in the world today. The need for an automated system for vehicle license plate number recognition is important for security challenges. Therefore, this paper provides a smart system for vehicle license number recognition using Computer Vision. The system was trained using images of vehicles license numbers as training data. The training images were first annotated using the Visual Graphic Generator (VGG) annotation tool, after the annotation process, the trained images were pre-processed using the OpenCV library for conversion and masking of images. TesseractOCR was then used in extracting just texts from the images. The pre-processed and segmented images were then used in training the Mask R-CNN from a pre-trained weight. The result of the proposed system shows how the Mask R-CNN model was trained in ten training steps. The mask R-CNN model obtained accuracy and a loss value for each training step. The mask R-CNN model was evaluated using both training and test data. For the training and testing data, the Mask R-CNN was evaluated in terms of accuracy and loss. The evaluation was done using graphs. The results from the graph show that the Mask R-CNN had a better accuracy result in both training and testing data. The accuracy for training data was that of 95.25% and the accuracy for the testing data was 97.69%. For real-time vehicle license plate number recognition, we deployed our proposed model to the web. Here, we built a web application that allows real-time surveillance video. Our model was tested on different vehicles in the car park. The result of the mask R-CNN on the test shows how the Mask R-CNN model was used in not just capturing and extracting the vehicle’s license plate number but predicting the characters that appeared on the vehicle’s license plate number. We also compared our proposed system with another existing system. The comparison was done in terms of accuracy, loss, and precision. The result of our proposed model gave us an accuracy of 97.69%, which is higher than the existing system (85%). This study can further be improved by using the Internet of Things in performing live video streaming and also providing a database system that will be storing the predicted vehicle numbers for cars that are detected.

Deep Learning Based License Plate Number Recognition for Smart Cities

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates. The current research paper presents an effective Deep Learning (DL)-based VLPNR called DL-VLPNR model to identify and recognize the alphanumeric characters present in license plate. The proposed model involves two main stages namely, license plate detection and Tesseract-based character recognition. The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model. Then, the characters in the detected number plate are extracted using Tesseract Optical Character Recognition (OCR) model. The performance of DL-VLPNR model was tested in this paper using two benchmark databases, and the experimental outcome established the superior performance of the model compared to other methods.

A Live-Video Automatic Number Plate Recognition (ANPR) System Using Convolutional Neural Network (CNN) with Data Labelling on an Android Smartphone

Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicles, tracking traffic violations, and parking management system. This paper discusses a live-video ANPR system using CNN developed on an Android smartphone embedded with a camera with limited resolution and limited processing power based on Malaysian license plate standards. In terms of system performance, in an ideal outdoor environment with good lighting and direct or slightly skewed camera angle, the recognition works perfectly with a computational time of 0.635 seconds. However, this performance is affected by poor lighting, extremely skewed angle of license plates, and fast vehicle movement.

An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks

One of the largest automotive sectors in the world is India. The number of vehicles traveling by road has increased in recent times. In malls or other crowded places, many vehicles enter and exit the parking area. Due to the increase in vehicles, it is difficult to manually note down the license plate number of all the vehicles passing in and out of the parking area. Hence, it is necessary to develop an Automatic License Plate Detection and Recognition (ALPDR) model that recognize the license plate number of vehicles automatically. To automate this process, we propose a three-step process that will detect the license plate, segment the characters and recognize the characters present in it. Detection is done by converting the input image to a bi-level image. Using region props the characters are segmented from the detected license plate. A two-layer CNN model is developed to recognize the segmented characters. The proposed model automatically updates the details of the car entering and exiting the parking area to the database. The proposed ALPDR model has been tested in several conditions such as blurred images, different distances from the cameras, day and night conditions on the stationary vehicles. Experimental result shows that the proposed system achieves 91.1%, 96.7%, and 98.8% accuracy on license plate detection, segmentation, and recognition respectively which is superior to state-of-the-art literature models.

Rekonstruksi citra kendaraan menggunakan SRCNN untuk peningkatan akurasi pengenalan pelat nomor kendaraan

Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.

Design of Vehicle License Monitoring System to Control Theft by using IOT

Smart transportation for urban cities can be done using Internet of Things (IOT). An automated object detection algorithm is used to identify the vehicle by using VLPR system. Identification of vehicle in heavy traffic or in parking lots is difficult and hence we propose a system by using RFID tags where the vehicle movement and vehicle license plate number can be obtained accurately. So by using IOT we can access the data from anywhere and the vehicle movement can be identified. Instead of using digital camera where due to external disturbance the images gets blurred, so we go for RFID where due to radio frequency transmission they stores the data. The performance of the device will not get degraded due to shadow noise, thunders and due to heavy speed. The main aim of proposed system is to check the vehicles license number and drivers vehicle license and to verify the vehicles RC book renewal.

Travel Time Reliability as a Level of Service Measure for Urban and Inter-Urban Corridors in India

The present study demonstrates the application and usefulness of travel time reliability as a level of service (LOS) measure for urban arterial and inter-urban highway corridors on Indian roads. For travel time estimation, automatic vehicle license plate number data were collected through TrafficMon system. This system is a fully video-based enforcement system designed to measure the speed of vehicles passing in view of the camera, and read the vehicular license plate number. This system was implemented at the entry and exit side of the identified three study corridors and data were collected during morning and evening peak periods. The data were analysed and various travel time reliability measures were evaluated. The study also attempts to correlate reliability measures such as planning time (PT), buffer time (BT), planning time index (PTI) and buffer time index (BTI) with volume-to-capacity ratio which is the most widely used LOS measure for Indian roads. Analysis of results indicated that at LOS B the travel time of intercity highway was 40–46 sec/km, whereas it was 64–80 sec/km and 75–135 sec/km for urban uninterrupted and interrupted corridors respectively. The planning time for LOS B was more on urban arterial corridors when compared to inter-urban corridor for the same width of the carriageway. The upper limits of LOS B for PT were 132 sec/km and 63 sec/km for uninterrupted urban corridor and intercity highway corridor respectively. Other parameters of reliability like PTI and BTI were also evaluated and their values for different ranges of volume-capacity ratio were presented for identified corridors.

MODEL MONITORING DAN IDENTIFIKASI PELANGGAR DI JALUR TRANSJAKARTA MENGGUNAKAN LIBRARY TESSERACT OCR PADA RASPBERRY PI 3 MODEL B

TransJakarta Bus Lane is a special lane that is only passed by TransJakarta bus and is not allowed for other riders entering and passing the lane. But by reason of avoiding congestion, some riders break through the Transjakarta bus lane. This has distrubed Transjakarta bus travel schedule. Several attempts have been made by the government to prevent riders from entering TransJakarta route by installing 50 cm separator in several corridors, making Moveable concrete barriere and since Monday, 25 Noveber 2013 has been applied a maximum fine of Rp 500,000 for both the two- and four-wheeled vehicles or more, entering into a special line of TransJakarta bus or busway lane refers to article 287, Law No. 22 of 2009. The purpose of this study is to monitor in real time the rider who commits a violation or breaks through TransJakarta bus lane. The monitoring system is built in the form of hardware model consisting of Raspberry Pi 3 Model B, ultrasonic sensor HC-SR04, webcamera, and software in the form of website applications. Data processing vehicle license plate number using tesseract ocr library on raspberry. When the hardware model is turned on the ultrasonic sensor HC-SR04 detects the distance of the vehicle object, at a distance of ≤ 10 cm webcamera shoots the vehicle license plate model. Images taken by webcamera processed by raspberry using tesseract ocr library through threshold process so it can be read. The results is displayed on the website application so that it can known the identity of the violator.

Niblack’s binarization method and its modifications to real-time applications: a review

Local binarization methods deal with the separation of foreground objects (textual content) and background noise (non-text) specifically at the pixel level. This is a much-explored field in the domain of documents image-processing that tends to separate the textual content from a degraded document. Since three decades, many local binarization methods have been developed to binarize documents images suffering from severe deteriorations. This paper presents a review of local binarization methods that are developed based on Niblack’s binarization method (NBM, developed in 1986) only. Further, this paper is a review of local binarization methods having more or less modifications to the original Niblack’s method, depending on the requirements of their model and the processed output. The modifications to NBM can be seen in various applications, such as deteriorated documents image binarization, manuscripts restoration, finding texts in video frames, revealing engraved wooden stamps, vehicle license plate number recognition, stained cytology nuclei detection and product barcodes reading. However, there could be a possibility of other applications using NBM with modifications based on the input images and the applications’ requirements.

C-ITS 환경에서 V2I 실현을 위한 버스 전용 차선 및 주행 차량 번호판 인식

최근 지능화된 사물들이 연결되는 네트워크를 통해 사람과 사물, 사물과 사물 간에 상호 소통하고 상황인식 기반의 지식이 결합되어 인공지능 서비스를 제공하는 사물인터넷 (IoT : Internet of Things) 환경이 급속도로 발전하고 있다. 이러한 사물인터넷의 발전과 더불어 C-ITS (Cooperative Intelligent Transport System) 환경에서 고속으로 이동하는 차량이 기존의 노변 인프라 외에 주행 중인 다른 차량까지 교통 인프라에 포함하여 차선 및 번호판 인식, 전방 사고 및 도로 공사 감지 등 쌍방향 정보 공유를 통해 효율적인 도로 주행을 함으로써 운전자에게 편리성과 안전성을 높여주고 나아가 교통 효율성을 높이고자 하는 연구가 활발히 진행되고 있다. 본 논문에서는 C-ITS 환경에서 고속도로 주행 시 버스전용 차선 인식 후 교통 인프라와 연계하여 버스전용 차선 내 주행차량의 주행 가능 여부를 판단하고 이에 따른 후속 조치에 관한 연구를 진행하였다. 버스전용차선 인식을 통해 버스전용 차로의 위치를 파악한 후 후속 차량의 정면 전방 및 측면 전방 차량의 번호판 인식을 진행하고 향후 교통 인프라로 하여금 인지하게 하는 방법에 관한 학습과 해당 실험결과를 제시하였다.

Effectiveness of government enforcement in driving restrictions: a case in Beijing, China

During the Olympic Games in 2008, a driving restriction based on vehicle license plate numbers was implemented in Beijing to mitigate air pollution and traffic congestion. Following the Games, the restriction was modified several times. This paper investigates the effects of two policy changes: a weakening policy change due to a shorter restricted time period, and a strengthening policy change due to a higher penalty for violators and the complementary car purchasing restriction. By employing a regression discontinuity design in a Tobit model, I find that the weakening policy change led to more pollution and the strengthening policy change improved air quality in restricted areas. Several robustness checks confirm the results. I also provide suggestive evidence that driving restrictions increased the use of public transportation and alleviated traffic congestion.

UHF RFID Tag Antenna for Vehicle License Plate Number (e-Plate)

In this research presents a new design of UHF RFID tag antenna for vehicle license plate number (e-plate). The proposed e-plate does not require another gadget or equipment since every vehicle is attached with a vehicle registration plate number and this e-plate embedded together. A low cost FR4 material has been used for its fabrication and there is performance improvement compared to the current tag antenna. The proposed antenna design works at 902-928 MHz frequency band for UHF RFID application with 3.8 dBi antenna gain. The antenna is rectangular in shape and has a dimension of 300 mm x 100 mm, which is usually the typical size of the conventional vehicle registration plate number. Acceptable responses were obtained in simulation at centre frequency of 915 MHz with reflection coefficient of -57.7 dB. The performance of proposed e-plate antenna was further tested by attaching a RFID tag chip and embedded it to the actual vehicle plate number. Initial testing on the field by attached e-plate on vehicle was achieved maximum reading range of up to 12 meters.

Driver anger on the information superhighway: A content analysis of online complaints of offensive driver behaviour

In recent years, several websites have been developed allowing drivers to post their complaints about other motorists online. These websites allow drivers to describe the nature of the offensive behaviour and to identify the offending motorist by vehicle type, colour, and license plate number. Some websites also ask drivers to list the location where the event took place and the exact date and time of the offence. The current study was a content analysis of complaints posted to RoadRagers.com between 1999 and 2007 (N=5624). The purpose of the study was to: (1) assess the research value of this novel data source; (2) demonstrate the value of content analysis to the study of driver behaviour; (3) further validate an existing coding scheme; (4) determine whether this new data source would replicate previous research findings regarding the most frequent types of driver complaints and temporal distribution of these reports; (5) provide recommendations for improved driver training and public safety initiatives based on these data. A coding scheme that was originally developed for an assessment of complaints submitted to the Ontario Provincial Police (OPP) (Wickens et al., 2005) was revised to accommodate the new dataset. The inter-rater reliability of the revised coding scheme as applied to the website complaints was very good (kappa=.85). The most frequently reported improper driver behaviours were cutting/weaving, speeding, perceived displays of hostility, and tailgating. Reports were most frequent on weekdays and during the morning and afternoon rush hour. The current study replicated several findings from the analysis of reports to the OPP, but possible differences in the sample and data collection method also produced some differences in findings. The value of content analysis to driver behaviour research and of driver complaint websites as a data source was demonstrated. Implications for driver safety initiatives and future research will be discussed.

Design of a real-time overload monitoring system for bridges and roads based on structural response

The problem of the general overload of vehicles that causing premature failure of bridges and roads is becoming more and more obvious. Structural behaviors of bridges and roads need real-time monitoring and diagnosis, timely damage detection, safety evaluation and necessary precautions, in order to prevent accidents such as the crack or the collapse of bridges and roads. However, the existing monitoring system is too expensive to be applied in low budget projects. This paper designs a portable, low-cost, low-power structural monitoring system, which is equipped with Polyvinylidene Fluoride (PVDF) traffic sensors, a central processing unit (CPU), an amplifier circuit, an Analog/Digital (A/D) converter circuit, a communication circuit, an alarm circuit, and analysis software. The proposed system can collect the critical data for a certain judge algorithm about the force of the pavement. An alarm will be signaled and the overweight data will be transmitted to the data center to make further analysis. At the same time, the license plate numbers of the overweight vehicles will be collected and sent to the traffic management department. The system has been tested and the results are satisfactory. Because of its features of simple structure, easy implementation, and low cost, the proposed system fills the application gaps of structural health monitoring in low-budget projects.

Development of a delay model for unsignalized intersections applicable to traffic assignment

This paper develops a model for estimating unsignalized intersection delays which can be applied to traffic assignment (TA) models. Current unsignalized intersection delay models have been developed mostly for operational purposes, and demand detailed geometric data and complicated procedures to estimate delay. These difficulties result in unsignalized intersection delays being ignored or assumed as a constant in TA models. Video and vehicle license plate number recognition methods are used to collect traffic volume data and to measure delays during peak and off-peak traffic periods at four unsignalized intersections in the city of Tehran, Iran. Data on geometric design elements are measured through field surveys. An empirical approach is used to develop a delay model as a function of influencing factors based on 5- and 15-min time intervals. The proposed model estimates delays on each approach based on total traffic volumes, rights-of-way of the subject approach and the intersection friction factor. The effect of conflicting traffic flows is considered implicitly by using the intersection friction factor. As a result, the developed delay model guarantees the convergence of TA solution methods. A comparison between delay models performed using different time intervals shows that the coefficients of determination, R 2, increases from 43.2% to 63.1% as the time interval increases from 5- to 15-min. The US Highway Capacity Manual (HCM) delay model (which is widely used in Iran) is validated using the field data and it is found that it overestimates delay, especially in the high delay ranges.

An Effective Regularization Method for Image Super Resolution

In this paper, we propose a new super resolution (SR) reconstruction method to handle license plate numbers of vehicles in real traffic videos. Recently, SR reconstruction shemes based on regularization have been demonstrated to be effective because SR reconstrction is an ill-posed problem. Working within this promising framework, the residual data (RD) term can be weighted according to the differences among the observed LR images in the SR reconstruction model. Moreover, L1 norm is used to measure the RD term in order to improve the robustness of our method. Experiments show the proposed method improves the subjective visual quality of the high resolution images.

A 3-D Transformation to Improve the Legibility of License Plate Numbers

In this paper, a novel three-dimensional transformation method for vehicle license plate number recognition is proposed. This method provides an efficient solution to normalize skew distorted vehicle license plate images. The Hough transform is used to estimate the license plate position and the normalization angle. After the three-dimensional transformation and normalization processes, the vehicle license plate numbers are recognized easily. Real vehicle license plate images are used to show the capability of the proposed method. The provided method is also useful for other skewed writings, such as the text printed on a suspect's shirt.

Child seating position and restraint use in three states

Objective—Because of risks from deploying airbags to children in front seats, extensive publicity has been aimed at getting them restrained and in rear seats. The objective of this study was...

PARTICIPATIVE EDUCATION FOR CHILDREN: AN EFFECTIVE APPROACH TO INCREASE SAFETY BELT USE

Vehicle license plate numbers and the shoulder belt use of front-seat occupants were recorded unobtrusively when parents delivered and picked up their children at a Montessori school during 5-day baseline, intervention, and follow-up phases. Practicing and presenting a 15-min safety belt skit increased the safety belt use of those 6 kindergarten children who were not consistent belt users 82% above their preintervention baseline belt use mean of 47%. The belt use of these children's parents (who watched the skit) increased to 56% above their baseline mean of 36%. Also, mean safety belt use of 11 primary school children who watched the skit increased to 70% above their baseline of 28%. Mean safety belt use of the older children's parents (who didn't watch the skit) remained at approximately 31% for each phase, regardless of whether children were vehicle occupants. The follow-up observations, taken 3 months after the intervention, revealed 60% belt use for the kindergartners, 48% for the primary school children, and 71% for the kindergartners' parents when the children were vehicle occupants but only 30% when the parents were driving alone.