Online license plate matching procedures using license-plate recognition machines and new weighted edit distance

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

doi:10.1016/j.trc.2011.11.003

Cited by 40 publications

(20 citation statements)

References 14 publications

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“…e normalized Levenshtein distance has rarely been used in transportation engineering. In most studies, this method is used for manipulation of a string that may consist of characters, variables, numbers, and so on, for example, the comparison between the license plates of vehicles [22], timeseries comparison [23], sequences of trip purposes, and cluster activity-travel patterns [24]. In the NLOD method, each row of the OD matrix is sorted according to its traffic flow.…”

Section: Optimization-based Methodsmentioning

confidence: 99%

A Structural Comparison between the Origin-Destination Matrices Based on Local Windows with Socioeconomic, Land-Use, and Population Characteristics

Zargari

Memarnejad

Mirzahossein

2021

Journal of Advanced Transportation

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The origin-destination (OD) matrices express the number and the pattern of trips distributed between OD pairs. OD matrix structural comparison can be used to identify different mobility patterns in the cities. A comparison of two OD matrices could express their difference from both numerical and structural aspects. Limited methods, such as the mean structural similarity (MSSIM) index and geographical window-based structural similarity index (GSSI), have been developed to compare the structural similarity (SSIM) of two matrices. These methods calculate the structural similarities of two OD matrices by grouping the OD pairs into local windows. The obtained results from the MSSIM entirely depend on the dimensions of the chosen windows. Meanwhile, the GSSI method only focuses on the geographical adjacency and correlation of zones while selecting local windows. Accordingly, this paper developed a novel method named Socioeconomy, Land-use, and Population Structural Similarity Index (SLPSSI) in which local windows are selected according to socioeconomic, land-use, and population properties for SSIM comparison of OD matrices. The proposed method was tested on Tehran’s OD matrix extracted from cell phone Geographic Position System (GPS) data. The advantage of this method over two previous ones was observed in determining the new pattern of trips on local windows and more precise detection of SSIM of the weekdays. The SLPSSI approach is up to 10 percent more accurate than the MSSIM method and up to 5.5 percent more accurate than the GSSI method. The proposed method also had a better performance on sparse OD matrices. It is capable of better determining the SSIM of sparse OD matrices by up to 8% compared with the GSSI method. Finally, the sensitivity analysis results indicate that the suggested method is robust and reliable since it is sensitive to applying both constant and random coefficients.

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Section: Optimization-based Methodsmentioning

confidence: 99%

A Structural Comparison between the Origin-Destination Matrices Based on Local Windows with Socioeconomic, Land-Use, and Population Characteristics

Zargari

Memarnejad

Mirzahossein

2021

Journal of Advanced Transportation

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“…These systems follow different approaches to locate vehicle number plate from vehicle and then to extract vehicle number from that image. Most of the ANPR systems are based on common approaches like artificial neural network (ANN) [5], [1], [6], [7][8], [9], [10], Probabilistic neural network (PNN) [11], Optical Character Recognition (OCR) [5], [12], [2], [13], [7], [14], Feature salient [15], MATLAB [16], Configurable method [17], Sliding concentrating window (SCW) [14], [8], BP neural network [18], support vector machine(SVM) [19], inductive learning [20], region based [21], color segmentation [22], fuzzy based algorithm [23], scale invariant feature transform (SIFT) [24], trichromatic imaging, Least Square Method(LSM) [25], [26], online license plate matching based on weighted edit distance [27] and color-discrete characteristics [28]. A case study of license plate reader (LPR) is well explained in [29].…”

Section: Automatic Number Plate Recognition (Anpr)mentioning

confidence: 99%

“…Feed forward back-propagation (BP) algorithm was used to train ANN. BP neural network based systems are proposed in [8], [18], [10] with the processing time of 0.06s, in [27]. In [46], HNN is applied to reduce ambiguity between the similar characters e.g.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Automatic Number Plate Recognition System (ANPR): A Survey

Patel¹,

Shah²,

Patel³

2013

IJCA

151

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Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) system as a one of the solutions to this problem. There are numerous ANPR systems available today. These systems are based on different methodologies but still it is really challenging task as some of the factors like high speed of vehicle, non-uniform vehicle number plate, language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Most of the systems work under these limitations. In this paper, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Towards the end of this paper, an extension to ANPR is suggested.

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“…Based on the characteristics above described, several methods have been proposed for ALPR systems, where each one has its advantages and disadvantages [10,11]. Most works of ALPR system [12,13], use the edge proprieties as features for localizing the license plate regions, and some of these works [14] capture the image of a vehicle carefully placed in front of the camera taking a clear image of the license plate. However, in a practical scenario, there may be huge variations in lighting conditions that make plate recognition even more difficult.…”

Section: Introductionmentioning

confidence: 99%

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

Dalarmelina

Teixeira

Meneguette

2019

Sensors

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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.

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Online license plate matching procedures using license-plate recognition machines and new weighted edit distance

Cited by 40 publications

References 14 publications

A Structural Comparison between the Origin-Destination Matrices Based on Local Windows with Socioeconomic, Land-Use, and Population Characteristics

A Structural Comparison between the Origin-Destination Matrices Based on Local Windows with Socioeconomic, Land-Use, and Population Characteristics

Automatic Number Plate Recognition System (ANPR): A Survey

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

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