An Online Self-Learning Algorithm for License Plate Matching

Oliveira-Neto, Francisco Moraes; Lee, Han; Jeong, Myong K.

doi:10.1109/tits.2013.2270107

Cited by 23 publications

(22 citation statements)

References 11 publications

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“…The paper by Akin and Akbas [3] describes the use of neural networks to model intersection crashes and intersection characteristics, such as, lighting, surface materials, etc. Taken together these papers show the use data mining to better understand the factors that can influence and improve safety at rail crossings.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

“…Similarly D'Andrea et al mined Twitter and used support vector machines to detect traffic events [5]. Another recent application of text mining is to license plate recognition [3]. These authors use Levenshtein text mining in combination with a Bayesian approach to increase the accuracy of automated license plate matching.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

“…These authors use Levenshtein text mining in combination with a Bayesian approach to increase the accuracy of automated license plate matching. Cao et al, use data mining in combination with rule-based and machine learning approaches to perform traffic sentiment analysis [3]. Speech processing and message feature extraction have been used for detection of intent in traveller screening [4].…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

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Analysing the Train Accident Injuries using Mining Techniques

Nireesha¹

2017

IJRASET

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Rail accidents describe a significant safety concern for the transportation industry in multiple countries. To better understand the contributors to these extreme accidents, the Federal Railroad Administration has established the railroads involved in accidents to submit reports that contain both fixed function entries and narratives that explain the characteristics of the accident. While a number of studies have noticed the fixed fields, nothing has done an extensive analysis of the narratives. This project describes the use of mining with a combination of techniques to automatically discover accident characteristics that can confess a better understanding of the contributors to the accidents. The results show that predictive accuracy for accident costs significantly improves on the use of features found by mining and predictive accuracy also improves through the use of modern ensemble methods. Importantly, this study furthermore shows through case examples how the findings from mining of the narratives can improve understanding of the contributors to rail accidents in ways not possible on only fixed field analysis of the accident reports.

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Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

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Analysing the Train Accident Injuries using Mining Techniques

Nireesha¹

2017

IJRASET

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“…Similarly D'Andrea et al mined Twitter and used support vector machines to detect traffic events [7]. Another recent application of text mining is to license plate recognition [8]. These authors use Levenshtein textmining in combination with a Bayesian approach to increase the accuracy of automated license plate matching.…”

Section: Related Workmentioning

confidence: 99%

Text Mining the Contributors to Rail Accidents

Brown

2016

IEEE Trans. Intell. Transport. Syst.

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Rail accidents represent an important safety concern for the transportation industry in many countries. In the 11 years from 2001 to 2012, the U.S. had more than 40 000 rail accidents that cost more than $45 million. While most of the accidents during this period had very little cost, about 5200 had damages in excess of $141 500. To better understand the contributors to these extreme accidents, the Federal Railroad Administration has required the railroads involved in accidents to submit reports that contain both fixed field entries and narratives that describe the characteristics of the accident. While a number of studies have looked at the fixed fields, none have done an extensive analysis of the narratives. This paper describes the use of text mining with a combination of techniques to automatically discover accident characteristics that can inform a better understanding of the contributors to the accidents. The study evaluates the efficacy of text mining of accident narratives by assessing predictive performance for the costs of extreme accidents. The results show that predictive accuracy for accident costs significantly improves through the use of features found by text mining and predictive accuracy further improves through the use of modern ensemble methods. Importantly, this study also shows through case examples how the findings from text mining of the narratives can improve understanding of the contributors to rail accidents in ways not possible through only fixed field analysis of the accident reports.Index Terms-Rail safety, safety engineering, latent Dirichlet allocation, partial least squares, random forests. 1524-9050

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“…Existing methods for improving the matching of plates recognized by LPR units rely on intensive manual data reduction, such that the misread plates are manually entered into the system [1]. To compensate for LPR misreading problem, we propose a new weight function based on a probability model to match the observed outcomes of a dual LPR setup [2].…”

Section: Literature Surveymentioning

confidence: 99%

Vehicle Plate Matching using License Plate Recognition based on Modified Levenshtein Edit Distance

Khan¹,

Ali²

2016

IJCA

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Vehicle License plate recognition (LPR) method is a fullgrown so far deficient approach used for computerized toll group and rapidity enforcement. Recently, an sophisticated matching approach that combines Bayesian likelihood and Levenshtein text-mining method was planned to improve the exactness of computerized vehicle license plate matching. The key module of this technique is what we known as PrecisionRecall curve, which contains the conditional probabilities of observing one character at one node for a given observed character at an additional station. Therefore, the evaluation of the performance constraint relies on the by hand extracted position truth of a large number of plates, which is an unwieldy and deadly process. To beat this negative aspect, in this cram, we propose an inventive novel LPM-MLED (License Plate Matching -Modified Levenshtein Edit Distance) method that removes the need for extracting ground truth by hand. The propose method perform well in the exactness in plate matching, in comparison with those generated from the meticulous manual method. Furthermore, this method outperforms their manual counterparts in plummeting false matching rates. The computational LPM-MLED technique is also cheaper and easier to implement and continues to improve and correct itself over time.

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An Online Self-Learning Algorithm for License Plate Matching

Cited by 23 publications

References 11 publications

Analysing the Train Accident Injuries using Mining Techniques

Analysing the Train Accident Injuries using Mining Techniques

Text Mining the Contributors to Rail Accidents

Vehicle Plate Matching using License Plate Recognition based on Modified Levenshtein Edit Distance

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