scholarly journals Sensor network based vehicle classification and license plate identification system

2009 ◽  
Author(s):  
Jan Frigo ◽  
Vinod Kulathumani ◽  
Sean Brennan ◽  
Ed Rosten ◽  
Eric Raby
Keyword(s):  
Sensor Network ◽  
Vehicle Classification ◽  
Identification System ◽  
License Plate

scholarly journals Classification & Detection of Vehicles using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 181-189
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Transportation Systems ◽  
Coarse Grained ◽  
Vehicle Classification ◽  
Training Dataset ◽  
License Plate ◽  
Original Dataset ◽  
Detection Of Objects

The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. However, theexisting methods of vehicle classification and detection are highly complex which provides coarse-grained outcomesbecause of underfitting or overfitting of the model. Due toadvanced accomplishmentsof the Deep Learning, it was efficiently implemented to image classification and detection of objects. This proposed paper come up with a new approach which makes use of convolutional neural networks concept in Deep Learning.It consists of two steps: i) vehicle classification ii) vehicle license plate recognition. Numerous classicmodules of neural networks hadbeen implemented in training and testing the vehicle classification and detection of license plate model, such as CNN (convolutional neural networks), TensorFlow, and Tesseract-OCR. The suggestedtechnique candetermine the vehicle type, number plate and other alternative dataeffectively. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original dataset (training) and enriched dataset (testing), this customized model(algorithm) can achieve best outcomewith a standard accuracy of around 97.32% inclassification and detection of vehicles. By enlarging the quantity of the training dataset, the loss function and mislearning rate declines progressively. Therefore, this proposedmodelwhich uses DeepLearning hadbetterperformance and flexibility. When compared to outstandingtechniques in the strategicImage datasets, this deep learning modelscan gethigher competitor outcomes. Eventually, the proposed system suggests modern methods for advancementof the customized model and forecasts the progressivegrowth of deep learningperformance in the explorationof artificial intelligence (AI) &machine learning (ML) techniques.

scholarly journals Residential Area Surveillance and Parking System

2020 ◽  
Vol 9 (5) ◽  
pp. 602-606
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Residential Area ◽  
High Rate ◽  
Identification System ◽  
License Plate ◽  
License Plate Recognition ◽  
Position Effects ◽  
Plain Text ◽  
Alert Message

The vehicles playing the vital role in our day to day life for transport, and some of the vehicles violates the traffic rules are also increasing, vehicle theft, unnecessary entering into highly restricted areas, increased number of accidents lead to increase in the rate of crime slowly. The vehicle had its own identity it should be recognized which plays the major role in the world. For recognition of the vehicles which are used commonly in the field of safety and security system, LPDR plays a major role and the vehicle registration number is recognized at some certain distance accurately. License Plate recognition is the most efficient and cost effective technique used for detection and recognition purposes. Automatic license plate recognition (ALPR) is used for finding the location of the license plate in the vehicle. These methods and techniques vary based on the conditions like, quality of the image, vehicle on a fine-tuned position, effects of lighting, type of image, etc. The objective is to design an efficient automatic conveyance identification system of sanctioned or unauthorized in the residential societies by utilizing the conveyance number plate. By getting the car image from the surveillance camera in the entrance, we recognizing the number plate and the characters are extracted using OCR (optical character recognition). It converts the character in the image to plain text. Then the plain text of the license plate is cross-verified with the database to check whether the vehicle belongs to residents or visitor. It sends the alert message to the security official when a new visitor request method in a residential area. The log details are stored separately for the resident and visitor in the database. It also provides the details about the parking area availability in the residential area. By calculating the number of vehicles in and out of the area, the detail or availability parking slot is displayed and it sis robust to the size, lighting effects with high rate of detection.

scholarly journals A Novel Approach to Vehicle Number Identification using Raspberry pi 3

2019 ◽  
pp. 64-67
Author(s):  
N. Varshini ◽  
Sumedha Kasarla ◽  
Shaik Subhani
Keyword(s):  
Real Time ◽  
Traffic Signals ◽  
Raspberry Pi ◽  
Identification System ◽  
License Plate ◽  
Text Format ◽  
Novel Approach ◽  
Image Conversion ◽  
Image Capturing ◽  
Conversion Technology

Vehicle Number Identification using Raspberry pi 3 is an image conversion technology which captures the license plate of a vehicle. The main aim is to make an effective and accurate license number plate identification system. This system is carried out and performed in the areas where traffic signals are present and the camera is placed on the signal which is connected to raspberry pi and it sends signals to the server and it can also be used in apartments or residencies for capturing all the vehicle numbers entering the building. This system at first detects the vehicle license plate and then captures it .It then converts the image into the text. The text of the license plate is displayed on the screen using the image conversion. Open CV and OCR are the two software's used for image capturing and conversion of that into text format respectively. The resulting data is then displayed on the screen and saved into a folder. The whole system is developed on Raspberry Pi desktop and its performance is used in real-time. It is observed from this experiment that the system mainly detects and captures the vehicle license plate, converts the image into text and displays it on the screen successfully.

scholarly journals A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 55
Author(s):  
Nicole do Vale Dalarmelina ◽  
Marcio Andrey Teixeira ◽  
Rodolfo I. Meneguette
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Transportation Systems ◽  
Identification System ◽  
License Plate ◽  
Optical Character ◽  
Smart Parking

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

The Design of a Car License Plate Identification System Based on AdaBoost Algorithm

2011 ◽  
Vol 181-182 ◽  
pp. 588-593
Author(s):  
Hong Wang ◽  
Xian Li ◽  
Shuang Liu
Keyword(s):  
False Positive Rate ◽  
Identification System ◽  
License Plate ◽  
Correct Identification ◽  
Integral Image ◽  
Large Database ◽  
Identification Rate ◽  
Adaboost Algorithm ◽  
Positive Rate ◽  
Correct Identification Rate

Design and implement a car license plate identification system with the applications of Viola and Jones algorithm. This algorithm which is based on the AdaBoost method is trained and optimized for the best performance using large database of car license plate images. The final license plate identification system obtained a cascade of classifiers consisting of 8 stages with 1310 Haar-like features. Once the license plates have sufficient visibility and there are no other objects similar to the plate in images, this system operates perfectly and shows high correct identification rate with low false positive rate. And as integral image allows the Haar-like features to be calculated very fast, the system also finished the identification rapidly.

scholarly journals Conjoint Vehicle License Plate Identification System

2020 ◽  
Vol 14 (1) ◽  
pp. 164-173
Author(s):  
Yair Wiseman
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Automatic Identification ◽  
Identification System ◽  
License Plate ◽  
Vehicle Identification ◽  
Parking Lot ◽  
High Level ◽  
Identification Techniques ◽  
The City

Background: An autonomous vehicle will go unaccompanied to park itself in a remote parking lot without a driver or a passenger inside. Unlike traditional vehicles, an autonomous vehicle can drop passengers off near any location. Afterward, instead of cruising for a nearby free parking, the vehicle can be automatically parked in a remote parking lot which can be in a rural fringe of the city where inexpensive land is more readily available. Objective: The study aimed at avoidance of mistakes in the identification of the vehicle with the help of the automatic identification device. Methods: It is proposed to back up license plate identification procedure by making use of three distinct identification techniques: RFID, Bluetooth and OCR with the aim of considerably reducing identification mistakes. Results: The RFID is the most reliable identification device but the Bluetooth and the OCR can improve the reliability of RFID. Conclusion: A very high level of reliable vehicle identification device is achievable. Parking lots for autonomous vehicles can be very efficient and low-priced. The critical difficulty is to automatically make sure that the autonomous vehicle is correctly identified at the gate.

scholarly journals Novel and Real-Time GPU Accelerated Speed Violation Detection and License Plate Identification System

2021 ◽  
Author(s):  
Udaya Dampage ◽  
KKN Hasantha ◽  
HADS Gimhana ◽  
HMTYN Bandara ◽  
SV Maddumage
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Specific Area ◽  
Superior Performance ◽  
Traffic Density ◽  
Maximum Speed ◽  
Identification System ◽  
License Plate ◽  
Starting Point ◽  
Violation Detection

Abstract Commuters lose a lot of time and effort due to the inefficiency of traffic management. Although nowadays most of the processes are automated, it seems a speed violation detection is the least focused area apart from using speed guns which the RADAR may make mistakes and yet, doing such will benefit people by saving their time and let them escape from the troublesome situations. To address this issue, a real-time solution by fully automating the process of detecting the speed violation and the license plates of the offenders is proposed in this paper. A vehicle approaching a specific area will be automatically identified and tracked from a reference starting point. Within the covered range of the camera according to the traffic density present at that instance, the maximum speed for a vehicle is estimated and the vehicles that exceed the stipulated limit are identified as a violation. The core part of the proposed system is license plate recognition. To properly extract the license plate with the best view to proceed with the identification process is another problem that needs to be focused on. We utilized deep neural networks in a novel way for the aforesaid purpose. As these neural networks consist of numerous parameters, we utilized GPU for processing to gain smoothness in real-time. Using our novel segmentation free license plate identification method which utilizes object detection principle to fully capture the speed violation along with its offender. Numerous field trials proved that the proposed methodology provides far superior performance levels compared to the conventional systems and the other existing methodologies, which will certainly cater to the demanding requirements of Transportation 4.0.

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