Entry and exit monitoring using license plate recognition

Felix, A. Yovan; Jesudoss, A.; Mayan, J. Albert

doi:10.1109/icstm.2017.8089156

Cited by 14 publications

(5 citation statements)

References 7 publications

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“…In such circumstances, deep neural networks (DNN) can be employed to discover the features automatically from raw data with no human interactions [73]. Therefore, DNNs have been successfully involved in many real-world applications, such as self-driving automobiles [74][75][76], speech recognition and processing [77][78][79][80], image recognition and processing [81][82][83], healthcare [84][85][86], character recognition [87][88][89][90], and many more [71,91].…”

Section: Machine-learning-based Modellingmentioning

confidence: 99%

Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling

et al. 2022

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Drying is a complex process of simultaneous heat, mass, and momentum transport phenomena with continuous phase changes. Numerical modelling is one of the most effective tools to mechanistically express the different physics of drying processes for accurately predicting the drying kinetics and understanding the morphological changes during drying. However, the mathematical modelling of drying processes is complex and computationally very expensive due to multiphysics and the multiscale nature of heat and mass transfer during drying. Physics-informed machine learning (PIML)-based modelling has the potential to overcome these drawbacks and could be an exciting new addition to drying research for describing drying processes by embedding fundamental transport laws and constraints in machine learning models. To develop such a novel PIML-based model for drying applications, it is necessary to have a fundamental understanding of heat, mass, and momentum transfer processes and their mathematical formulation of drying processes, in addition to data-driven modelling knowledge. Based on a comprehensive literature review, this paper presents two types of information: fundamental physics-based information about drying processes and data-driven modelling strategies to develop PIML-based models for drying applications. The current status of physics-based models and PIML-based models and their limitations are discussed. A sample PIML-based modelling framework for drying application is presented. Finally, the challenges of addressing simultaneous heat, mass, and momentum transport phenomena in PIML modelling for optimizing the drying process are presented at the end of this paper. It is expected that the information in this manuscript will be beneficial for further advancing the field.

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Section: Machine-learning-based Modellingmentioning

confidence: 99%

Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling

et al. 2022

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“…In addition, high resolution cameras need to be integrated, allowing robust algorithms to reduce processing times and increase recognition capabilities. Yet another avenue is Obscure character recognition, since there are a lot similarities in characters like the pairs-(O,0), (P,B), (Z,2), (S,5), (3,8), (B,8), (P,R), (D,O), (1,I), (Q,O), (C,G), (A,4), (K,X), (F,E), (b,6), (q,9), (p,b), (V,W), (X,Y), (V,U), (6,8), (5,3), (5,8), (0,8), (3,9), (4,9) etc. The similarities, together with impairments, can easily deceive the optical character recognition mechanism if there is small tilt, fonts change, broken, snow or dirt on characters or if the image is acquired at different angles.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

“…Whilst ANPR technology can come in many different packages, they all perform the same basic function which is to provide a highly accurate system of reading a vehicle without human intervention. It is utilized in very diverse applications such as access control, parking management, tolling, user billing, delivery tracking, traffic management, policing and security services, customer services and directions, the red light and lane enforcement, queue length estimation, and many other services [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Figure 1 shows the basic system diagram of a fixed and mobile ANPR technology.…”

Section: Introductionmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

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“…Moreover, LPR has been applied to many fields, such as urban surveillance, parking management, and intelligent transportation. A vehicle attendance monitoring system with a high practical value was developed by correctly identifying the LP [ 10 ]. A smart parking management system was implemented to improve the inefficient management problems left by the traditional parking management, in which LPR plays a great role [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Robust Korean License Plate Recognition Based on Deep Neural Networks

Wang

Dang

et al. 2021

Sensors

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With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include (1) a robust LPR system with the integration of three pre-processing techniques (defogging, low-light enhancement, and super-resolution) that can effectively recognize the LP under various conditions, (2) the establishment of two original Korean LPR approaches for different scenarios, including whole license plate recognition (W-LPR) and single-character license plate recognition (SC-LPR), and (3) the introduction of two Korean LPR datasets (synthetic data and real data) involving a new type of LP introduced by the Korean government. Through several experiments, the proposed LPR framework achieved the highest recognition accuracy of 98.94%.

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Entry and exit monitoring using license plate recognition

Cited by 14 publications

References 7 publications

Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling

Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Robust Korean License Plate Recognition Based on Deep Neural Networks

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