E-Security System for Vehicle Number Tracking at Parking Lot (Application for VNIT Gate Security)

Thangallapally, Sai Krishna; Maripeddi, Raghupathi; Banoth, Vamshi Krishna; Cheggoju, Naveen; Satpute, V. R.

doi:10.1109/sceecs.2018.8546903

Cited by 10 publications

(5 citation statements)

References 3 publications

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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%

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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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Section: Conclusion and Future Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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“…Thangallapally et al [20] implemented a technique to recognize the characters on number plates and to upload details into a server. This, in turn, was segregated to extract the image of the vehicle number plate.…”

Section: Related Workmentioning

confidence: 99%

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

et al. 2021

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Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.

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“…The final license plate location is approved [19]. Finally, the Optical Character Recognition (OCR) segmentation [20] characters and number plate currently used to identify numbers in the NP area will be transmitted from the candidate area to the system [22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Type-2 Fuzzy Based Tesseract Text Relative Recognition Algorithm for Number Plate Identification

Kavitha¹,

Preethi²

2020

NPSC

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Automatic number plate authentication is a mass tracking system that captures vehicles' images and authenticates their license number. Number plate detection can be used in many applications such as electronic payment methods (toll payment, parking payment), freeway, arterial monitoring systems for traffic monitoring, and stolen vehicles. Several methods have been proposed in previous studies to solve the following challenges (location size, size, color, font, opacity, gradient, etc.) by retrieving information from the captured image. However, each method has its drawback between the accuracy of the detection and the processing speed. This ambiguous inference system only gives an estimate of the limit plate of the clear hypothetical system answer. These methods work at different times for a different number of models. The Type2 Fuzzy based TesseractText Relative Recognition Algorithm (TTRR) obtains the image dataset from the JSON (JavaScript Object Notation) file and processes it through the link in the dataset. In the proposed detection, the method uses ambiguous logic classifiers that help identify plates and characters with excellent accuracy and processing speed. Compared to many significant contributions, Type2 Fuzzy based TTRR yields better results.

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E-Security System for Vehicle Number Tracking at Parking Lot (Application for VNIT Gate Security)

Cited by 10 publications

References 3 publications

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Type-2 Fuzzy Based Tesseract Text Relative Recognition Algorithm for Number Plate Identification

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