Vehicle License Plate Image Preprocessing Strategy Under Fog/Hazy Weather Conditions

2021 ◽  
pp. 277-282
Author(s):  
S. Som ◽  
P. K. Gayen ◽  
S. Bakshi ◽  
S. Mondal
Keyword(s):  
Weather Conditions ◽  
License Plate ◽  
Image Preprocessing

The Character Recognition of Vehicle's License Plate Based on BP Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 3805-3808 ◽  
Author(s):  
Wen Bo Liu ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Character Recognition ◽  
License Plate ◽  
Image Preprocessing ◽  
Matrix Operations ◽  
Neural Network Algorithm ◽  
Character Segment

This paper based on license plate image preprocessing ,license plate localization, and character segment ,using BP neural network algorithm to identify the license plate characters. Through k-l algorithm of characters on the feature extraction and recognition of license plate character respectively then taking the extraction of license plate character features into the character classifier to the training. When the end of training, extracting the net-work weights and offset matrix, and storing in the computer. To take the identified character images input to the MATLAB, and with the preservation weights and offset matrix operations, obtain the final results of recognition.

Development of a Stop-Line Violation Detection System for Indian Vehicles

2013 ◽  
pp. 200-227
Author(s):  
Satadal Saha ◽  
Subhadip Basu ◽  
Mita Nasipuri
Keyword(s):  
Recognition Accuracy ◽  
Detection System ◽  
Back Propagation ◽  
Real Life ◽  
Weather Conditions ◽  
Back Propagation Neural Network ◽  
License Plate ◽  
Test Environment ◽  
Localization Accuracy ◽  
Stop Line

In the present work, the authors designed and developed a complete system for generating the list of all violating vehicles that has violated the stop-line at a road crossing automatically from video snapshots of road-side surveillance cameras using background subtraction technique. It then localizes the license plates of the vehicles by analyzing the vertical edge map of the images, segments the license plate characters using connected component labeling algorithm, and recognizes the characters using back propagation neural network. Considering round-the-clock operations in a real-life test environment, the developed system could successfully track 92% images of vehicles with violations on the stop-line in a red traffic signal. The performance of the system is evaluated with a dataset of 4717 images collected from 13 different camera views in 4 different environmental conditions. The authors have achieved around 92% plate localization accuracy over different views and weather conditions. The average plate level recognition accuracy of 92.75% and character level recognition accuracy of 98.76% are achieved over the localized vehicle images.

The Vehicle License Plate Localization System Based on LabVIEW

2011 ◽  
Vol 393-395 ◽  
pp. 471-475
Author(s):  
Yu Yuan ◽  
Cong Ming Li ◽  
Bao Liang Li
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Computer Vision System ◽  
Image Preprocessing ◽  
Labview Software

License plate recognition is an important application topic of computer vision and pattern recognition technology in intelligent transportation field. Recognition system, which can automatically extract the license plate and segment the characters from an image, then recognize the characters, is a special computer vision system based on specific targets for the object. Generally, the system consists of hardware and software. In recent years, with the rapid development of LabVIEW software, LabVIEW not only has powerful data processing function, but also provides a lot of various kits. This paper is mainly to complete the functions of image preprocessing, license plate localization and license plate segmentation etc., by processing acquired image based on LabVIEW software programming.

scholarly journals Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Narasimha Reddy Soora ◽  
Parag S. Deshpande
Keyword(s):  
Detection System ◽  
Weather Conditions ◽  
Input Image ◽  
License Plate ◽  
Success Rates ◽  
Still Images ◽  
Geometrical Properties ◽  
Detection Systems ◽  
Character Extraction ◽  
Media Lab

Most of the existing license plate (LP) detection systems have shown significant development in the processing of the images, with restrictions related to environmental conditions and plate variations. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle multiple LPs of many countries and any vehicle, in an open environment and all weather conditions, having different plate variations. This paper presents a novel LP detection method using different clustering techniques based on geometrical properties of the LP characters and proposed a new character extraction method, for noisy/missed character components of the LP due to the presence of noise between LP characters and LP border. The proposed method detects multiple LPs from an input image or video, having different plate variations, under different environmental and weather conditions because of the geometrical properties of the set of characters in the LP. The proposed method is tested using standard media-lab and Application Oriented License Plate (AOLP) benchmark LP recognition databases and achieved the success rates of 97.3% and 93.7%, respectively. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark LP databases.

scholarly journals Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Moon Kyou Song ◽  
Md. Mostafa Kamal Sarker
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Computational Time ◽  
License Plate ◽  
Two Stage ◽  
Adaptive Boosting ◽  
Image Preprocessing ◽  
Adaboost Algorithm ◽  
Processing Techniques

License plate (LP) detection is the most imperative part of the automatic LP recognition system. In previous years, different methods, techniques, and algorithms have been developed for LP detection (LPD) systems. This paper proposes to automatical detection of car LPs via image processing techniques based on classifier or machine learning algorithms. In this paper, we propose a real-time and robust method for LPD systems using the two-stage adaptive boosting (AdaBoost) algorithm combined with different image preprocessing techniques. Haar-like features are used to compute and select features from LP images. The AdaBoost algorithm is used to classify parts of an image within a search window by a trained strong classifier as either LP or non-LP. Adaptive thresholding is used for the image preprocessing method applied to those images that are of insufficient quality for LPD. This method is of a faster speed and higher accuracy than most of the existing methods used in LPD. Experimental results demonstrate that the average LPD rate is 98.38% and the computational time is approximately 49 ms.

scholarly journals TECHNICAL MEANS OF AIRINTELLIGENCE TO ENSURE THE PHYSICAL SECURITY OF INFORMATION ACTIVITIES

2021 ◽  
Vol 12 (4) ◽  
pp. 143-150
Author(s):  
Artem Platonenko ◽  
Volodymyr Sokolov ◽  
Pavlo Skladannyi ◽  
Heorhii Oleksiienko
Keyword(s):  
Thermal Imaging ◽  
Critical Infrastructure ◽  
Weather Conditions ◽  
Unmanned Vehicles ◽  
License Plate ◽  
Thermal Imager ◽  
Camera Model ◽  
Thermal Imagers ◽  
Private And Public

This article is devoted to highlighting the real practical capabilities of UAV thermal imaging cameras, which allow you to effectively and safely identify potentially dangerous objects that may threaten the object of information activities, or the safety of citizens or critical infrastructure of Ukraine. Based on many years of flight experience and training of specialists for private and public institutions, it was decided to compare the quality characteristics and capabilities of detection, recognition and identification of objects using modern unmanned vehicles. To ensure public safety and control of the territory, there are models with multiple optical zoom, which from a distance of 500 m allow to recognize the license plate of the car, or versions with thermal imager, which in night can help see the car, the temperature difference against other cars, and the fact that a person comes out of it. Test flights were performed at altitudes from 15 to 100 m, in the open, without the presence of bushes, trees or obstacles. Depending on the camera model and weather conditions, the figures obtained may differ significantly. The main advantages and differences in the quality of thermal imaging cameras for UAVs are described. The quality of the obtained image is demonstrated on real examples and under the same conditions. A number of requirements have been developed for shooting a quadcopter with thermal imagers of objects such as a car and a person from different heights, according to Johnson's criteria, and a work plan has been developed for further research to prepare and provide effective recommendations for pilots using this technique territories of objects of information activity and during performance of service in air reconnaissance units of law enforcement agencies of Ukraine.

scholarly journals Information fusion in recognition of Saudi Arabia license plates

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Saudi Arabia ◽  
Image Quality ◽  
Information Fusion ◽  
Weather Conditions ◽  
License Plate ◽  
License Plate Recognition ◽  
Kingdom Of Saudi Arabia ◽  
Parking Lots ◽  
Arabic And English

The license plate recognition (LPR) system in Saudi Arabia is a system used to identify vehicle license plates automatically. It is used in many places such as airports, highways, and parking lots. The efficiency of the system depends on the image quality, weather conditions, location of plates, and the variations of license plates. The license plates in the Kingdom of Saudi Arabia are different from other license plates in other countries because they are written in both Arabic and English languages. This could be exploited to integrate the recognition results from both languages in a way to increase the efficiency of the system and reduce the errors that could affect the recognition of license plates. Instead of one LPR system, we have two independent LPR systems, and the results of both systems could be fused to increase the system's ability of reading cars’ plates

scholarly journals Effects of Challenging Weather and Illumination on Learning-Based License Plate Detection in Noncontrolled Environments

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
A. Rio-Alvarez ◽  
J. de Andres-Suarez ◽  
M. Gonzalez-Rodriguez ◽  
D. Fernandez-Lanvin ◽  
B. López Pérez
Keyword(s):  
Weather Conditions ◽  
License Plate ◽  
License Plate Recognition ◽  
Training Set ◽  
Test Set ◽  
Number Of Factors ◽  
Controlled Environments ◽  
Low Illumination ◽  
License Plate Detection ◽  
Training Sets

License Plate Detection (LPD) is one of the most important steps of an Automatic License Plate Recognition (ALPR) system because it is the seed of the entire recognition process. In indoor controlled environments, there are many effective methods for detecting license plates. However, outdoors LPD is still a challenge due to the large number of factors that may affect the process and the results obtained. It is an evidence that a complete training set of images including as many as possible license plates angles and sizes improves the performance of every classifier. On this line of work, numerous training sets contain images taken under different weather conditions. However, no studies tested the differences in the effectiveness of different descriptors for these different conditions. In this paper, various classifiers were trained with features extracted from a set of rainfall images using different kinds of texture-based descriptors. The accuracy of these specific trained classifiers over a test set of rainfall images was compared with the accuracy of the same descriptor-classifier pair trained with features extracted from an ideal conditions images set. In the same way, we repeat the experiment with images affected by challenging illumination. The research concludes, on one hand, that including images affected by rain, snow, or fog in the training sets does not improve the accuracy of the classifier detecting license plates over images affected by these weather conditions. Classifiers trained with ideal conditions images improve the accuracy of license plate detection in images affected by rainfalls up to 19% depending on the kind of extracted features. However, on the other hand, results evidence that including images affected by low illumination regardless of the kind of the selected feature increases the accuracy of the classifier up to 29%.

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