Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition

Ullah, Farman; Anwar, Hafeez; Shahzadi, Iram; Rehman, Ata Ur; Mehmood, Shizra; Niaz, Sania; Awan, Khalid Mahmood; Khan, Ajmal; Kwak, Daehan

doi:10.3390/s19133015

Cited by 21 publications

(14 citation statements)

References 33 publications

(36 reference statements)

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“…The general process of automatic number plate recognition can be summarized in several well-defined steps [ 9 , 10 ]. Each step involves a different set of algorithms and/or considerations: Vehicle image capture: This step has a critical impact on the subsequent steps since the final result is highly dependent on the quality of the captured images.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis

Álvarez-Bazo

Sánchez-Cambronero

Vallejo

et al. 2020

Sensors

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In recent years, different techniques to address the problem of observability in traffic networks have been proposed in multiple research projects, being the technique based on the installation of automatic vehicle identification sensors (AVI), one of the most successful in terms of theoretical results, but complex in terms of its practical application to real studies. Indeed, a very limited number of studies consider the possibility of installing a series of non-definitive plate scanning sensors in the elements of a network, which allow technicians to obtain a better conclusions when they deal with traffic network analysis such as urbans mobility plans that involve the estimation of traffic flows for different scenarios. With these antecedents, the contributions of this paper are (1) an architecture to deploy low-cost sensors network able to be temporarily installed on the city streets as an alternative of rubber hoses commonly used in the elaboration of urban mobility plans; (2) a design of the low-cost, low energy sensor itself, and (3) a sensor location model able to establish the best set of links of a network given both the study objectives and of the sensor needs of installation. A case of study with the installation of as set of proposed devices is presented, to demonstrate its viability.

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Section: Introductionmentioning

confidence: 99%

A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis

Álvarez-Bazo

Sánchez-Cambronero

Vallejo

et al. 2020

Sensors

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“…In the context of image, the information content is represented by the pixel intensity values that range from 0 to

where

. The histogram of pixel intensity values [ 22 ] represents the number of pixels per intensity value as shown in Equation ( 1 ).

Each bin in this histogram gives the count of that particular intensity value as

.…”

Section: Methodsmentioning

confidence: 99%

Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters

Anwar

Ullah

Iqbal

et al. 2019

Entropy

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We perform image-based denomination recognition of the Pakistani currency notes. There are a total of seven different denominations in the current series of Pakistani notes. Apart from color and texture, these notes differ from one another mainly due to their aspect ratios. Our aim is to exploit this single feature to attain an image-based recognition that is invariant to the most common image variations found in currency notes images. Among others, the most notable image variations are caused by the difference in positions and in-plane orientations of the currency notes in images. While most of the proposed methods for currency denomination recognition only focus on attaining higher recognition rates, our aim is more complex, i.e., attaining a high recognition rate in the presence of image variations. Since, the aspect ratio of a currency note is invariant to such differences, an image-based recognition of currency notes based on aspect ratio is more likely to be translation- and rotation-invariant. Therefore, we adapt a two step procedure that first extracts a currency note from the homogeneous image background via local entropy and range filters. Then, the aspect ratio of the extracted currency note is calculated to determine its denomination. To validate our proposed method, we gathered a new dataset with the largest and most diverse collection of Pakistani currency notes, where each image contains either a single or multiple notes at arbitrary positions and orientations. We attain an overall average recognition rate of 99% which is very encouraging for our method, which relies on a single feature and is suited for real-time applications. Consequently, the method may be extended to other international and historical currencies, which makes it suitable for business and digital humanities applications.

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“…A typical AVLPR system consists of three stages: detection or localization of the region of interest (i.e., the license plate from the image), character extraction from the license plate, and recognition of those characters [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Islam

Raj

Islam

et al. 2020

Sensors

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Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

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Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition

Cited by 21 publications

References 33 publications

A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis

A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis

Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

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