Download Free A Real Time Implementation Of License Plate Recognition Lpr System Book in PDF and EPUB Free Download. You can read online A Real Time Implementation Of License Plate Recognition Lpr System and write the review.

Master's Thesis from the year 2010 in the subject Engineering - Computer Engineering, grade: A+, Gandhi Institute of Engineering and Technology, language: English, abstract: With increasing number of population and higher rate of development the problem of road accident is also increasing rapidly. So the basic concept is to develop a model that can be useful as a security system in the society and can monitoring the vehicle speed. A License Plate Recognition (LPR) System is one kind of an Intelligent Transport monitoring System and is of considerable interest because of its potential applications in highway electronic toll collection and traffic monitoring systems. This type of applications puts high demands on the reliability of an LPR System. A lot of work has been done regarding LPR systems for Korean, Chinese, European and US license plates that generated many commercial products. However, little work has been done for Indian license plate recognition systems. The purpose of this thesis was to develop a real time application which recognizes license plates from cars at a gate, for example at the entrance of a parking area or a border crossing. The system, based on regular PC with video camera, catches video frames which include a visible car license plate and processes them. Once a license plate is detected, its digits are recognized, displayed on the User Interface or checked against a database. The focus is on the design of algorithms used for extracting the license plate from a single image, isolating the characters of the plate and identifying the individual characters. The proposed system has been implemented using Vision Assistant 7,1 and LabVIEW 7,1. The performance of the system has been investigated on real images of about 100 vehicles. The recognition of about 98% vehicles shows that the system is quite efficient.
Explores issues concerning license plate reader technology: funding, implementation, types of use, data retention policies, and privacy concerns.
License Plate (LP) is the unique identification of a car. License Plate Recognition (LPR) is a method used by a computer to convert digital images of vehicle license plates into text. LPR have a wide range of applications. Among these applications: traffic control, parking, access control, border control, and stolen cars tracking. This work aims to design a LPR for the Iraqi license plates. It consists of three basic stages (preprocessing, LP localization and LP recognition). Since the images of the vehicles are taken in different day time, then the first stage in the proposed LPR is "preprocessing stage" which involves image binarization and image segmentation. The second stage is called "LP localization" where the accurate location of the LP in the digital image will be determined. The new used algorithm for locating the LP depends on the geometrical features of the LP. Then The LP is rotated and adjusted using affine transform. The last stage is "LP recognition" in which the numerals and characters of the LP are recognized into text. The two method used for the recognition of the numerals and characters are moment based and local density distribution based.
Automatic License Plate Recognition (LPR) is a basic computer vision problem. LPR has been thoroughly explored and is widely considered to be a well-understood problem. LPR systems are widely deployed all across the globe for a variety of situations. LPR systems perform poorly in the situations where the license plate is ambiguous, forged, or damaged. Similarly, LPR systems cannot work if only partial license plate information is available or if only vehicle's description is available; hit and run, hot pursuit, and amber alert are a few of the situations where a vehicle's description is often available. A Vehicle Make and Model Recognition (VMMR) system can provide great value in terms of vehicle monitoring and identification based on vehicle appearance instead of the vehicles' attached license plate. A VMMR system and an LPR system can be used to complement each other. A real-time VMMR system is an important component of many Intelligent Transportation System (ITS) applications, such as automatic vehicle surveillance, traffic management, driver assistance systems, traffic behavior analysis, and traffic monitoring, etc. The VMMR system can reduce the cost for such applications. VMMR systems have a unique set of challenges and issues. A few of the challenges are related to computer vision includes image acquisition, variations in lighting and illuminations, variations in weather, occlusion, shadows, and reflections, etc. A few of the challenges are due to the nature of the problem, such as the large variety of vehicles, the inter-class and intra-class similarities, addition/deletion of vehicles models over time, etc. The VMMR system is a multi-class classification/recognition problem; thus the selection of machine learning algorithm for robust and reliable VMMR system is another challenging task. In this thesis, we present a unique and robust real-time VMMR system which can handle the challenges described above and recognize vehicles with high accuracy. We extract image features from vehicle images and create many different feature vectors to represent the dataset. We use two existing classification algorithms, Random Forest and Support Vector Machine, in our work. We also proposed a two-level Support Vector Machine classification algorithm in our work. We use a realistic dataset to test and evaluate the proposed VMMR system. The vehicles' images in the dataset reflect realworld situations such as different weather conditions, different lighting exposure, occluded images (e.g. pedestrians), and different viewing angles, etc. The proposed VMMR system recognizes vehicles on the basis of make, model, and generation (group of consecutive manufacturing years) while the existing VMMR systems can only identify the make and model. Image feature descriptors are used to represent the vehicle dataset. Image features can be broadly divided into two categories: the first type of features is extracted using the prominent and distinctive points/patches in the image and uses the selected points/patches to represent the image while the second type of features uses the entire image for feature extraction and representation. We create optimized visual dictionaries to encode images for the first type of features. We also compare the proposed system with the existing VMMR research. The underlying goal of the proposed VMMR system is centered on discovering the ability of supervised learning to resolve the computer vision problem that results from the stringent limitations of the problem environment. The proposed real-time VMMR system can produce valuable information for law enforcement agencies.
Probe vehicle and floating traveler data can provide more detailed information about highway use across a roadway network than traditional transportation data sources. However, there are numerous concerns about accuracy, e.g., road user coverage, locational accuracy, and aggregation methods. To address these concerns, evaluations must be completed using a highly accurate data collection method to capture ideal ground truth. For the purpose of this dissertation, license plate recognition (LPR) technology is considered to be the suitable collection method for, and in lieu of, the all ground truth. The data can be obtained using a pair of mobile LPR units to automatically acquire and record license plates at sequential locations along a study route. LPR acquired license plates are then matched automatically by means of a self-learning text-mining algorithm. The algorithm relies on the weighted edit distances of each license plate character to drastically increase the number of correctly matched license plates (97% matching rate with 1% false-positives). To ensure that LPR technology is the best option for the evaluation of real-time data, the license plate matching algorithm requires enhancements to improve matching accuracy and learning speed. To address the required enhancements, this dissertation evaluates the initial matching process of the algorithm to help increase the speed of learning and matching of license plates. This was completed by updating the starting association matrix- the probability matrix which supplies the similarity measure for the edit distance calculation to determine the likelihood of a match between two associated LPR stations. To further enhance the matching algorithm, the research sought to improve on the procedure for estimating association matrices for problematic LPR stations by deriving an association matrix for a pair of LPR stations. Lastly, the LPR technology and the matching algorithm are employed to capture ground truth and employed to determine the key considerations when evaluating real-time travel times. The overall results are a drastic reduction in learning time, an increase in matching accuracy at problematic LPR stations, and a strong understating of the key considerations when using LPR as ground truth.
License plate recognition system (LPR) is an image processing technology used to identify vehicle by their license plate. This technology is gaining popularity in security and traffic installation. Much research has already been done for the recognition of Korean, Chinese, European, American and other license plates. This work presents license plate recognition method pertaining to India as an application of image processing, i.e. the images of license plate are taken and extract the features of license plates for recognition. This work first presents some applications of license plate recognition system. Next, the elements of a typical LPR system are discussed followed by the description of working principle of a typical LPR system. Then structure of proposed license plate recognition system is then presented. The chapter ends with a brief over view of the rest of the work.
The two-volume set LNCS 11961 and 11962 constitutes the thoroughly refereed proceedings of the 25th International Conference on MultiMedia Modeling, MMM 2020, held in Daejeon, South Korea, in January 2020. Of the 171 submitted full research papers, 40 papers were selected for oral presentation and 46 for poster presentation; 28 special session papers were selected for oral presentation and 8 for poster presentation; in addition, 9 demonstration papers and 6 papers for the Video Browser Showdown 2020 were accepted. The papers of LNCS 11961 are organized in the following topical sections: audio and signal processing; coding and HVS; color processing and art; detection and classification; face; image processing; learning and knowledge representation; video processing; poster papers; the papers of LNCS 11962 are organized in the following topical sections: poster papers; AI-powered 3D vision; multimedia analytics: perspectives, tools and applications; multimedia datasets for repeatable experimentation; multi-modal affective computing of large-scale multimedia data; multimedia and multimodal analytics in the medical domain and pervasive environments; intelligent multimedia security; demo papers; and VBS papers.
This book constitutes the refereed proceedings of the 9th Dortmund Fuzzy Days, Dortmund, Germany, 2006. This conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence. The papers presented here, all thoroughly reviewed, are devoted to foundational and practical issues in fuzzy systems, neural networks, evolutionary algorithms, and machine learning and thus cover the whole range of computational intelligence.