Temporal matching prior network for vehicle license plate detection and recognition in videos

Yoo, Seok Bong; Han, Mikyong

doi:10.4218/etrij.2019-0245

Cited by 12 publications

(10 citation statements)

References 13 publications

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“…In [12], a method of identification and recognition of license plates for streaming video is proposed. The method is based on a neural network with feedback.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The a priori values of direct and bidirectional matching between sequential video streaming frames are properly combined with layer structures that are specifically designed to identify license plates. Study [12] provides a set of video frames for deep learning of the proposed network. The advantage of [12] is that during network training, data augmentation is performed based on the rotation of the image.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Study [12] provides a set of video frames for deep learning of the proposed network. The advantage of [12] is that during network training, data augmentation is performed based on the rotation of the image. The disadvantage of the method reported in [12] is the need to perform a multicycle adjustment of the internal elements of the network and the connection between them.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 2 more Smart Citations

Devising a method for processing the image of a vehicle's license plate when shooting with a smartphone camera

Khudov

Makoveichuk

Misiuk

et al. 2022

EEJET

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This paper reports an improved method for processing the image of a vehicle's license plate when shooting with a smartphone camera. The method for processing the image of a vehicle's license plate includes the following stages: – enter the source data; – split the video streaming into frames; – preliminary process the image of a vehicle's license plate; – find the area of a vehicle's license plate; – refine character recognition using the signature of a vehicle's license plate; – refine character recognition using the combined results from frames in the streaming video; – obtain the result of processing. Experimental studies were conducted on the processing of images of a vehicle's license plate. During the experimental studies, the license plate of a military vehicle (Ukraine) was considered. The original image was the color image of a vehicle. The results of experimental studies are given. A comparison of the quality of character recognition in a license plate has been carried out. It was established that the improved method that uses the combined results from streaming video frames works out efficiently at the end of the sequence. The improved method that employs the combined results from streaming video frames operates with numerical probability vectors. The assessment of errors of the first and second kind in processing the image of a license plate was carried out. The total accuracy of finding the area of a license plate by known method is 61 % while the improved method's result is 76 %. It has been established that the minimization of errors of the first kind is more important than reducing errors of the second kind. If a license plate is incorrectly identified, these results would certainly be discarded at the character recognition stage.

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“…In [12], a method of identification and recognition of license plates for streaming video is proposed. The method is based on a neural network with feedback.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Devising a method for processing the image of a vehicle's license plate when shooting with a smartphone camera

Khudov

Makoveichuk

Misiuk

et al. 2022

EEJET

View full text Add to dashboard Cite

show abstract

“…Two-staged process usually detects and crops the region of interest (ROI) around LP to eliminate any unwanted background details, then only attempt to recognise the characters on the cropped LP images with optical character recognition (OCR). OCR could be based on hard-coded algorithms such as connected component analysis, local binary pattern, temporal matching [27], or CNN-based classification. However, the image processing pipeline of ALPR had been shifting to one-staged process with the emergence of ML, extracting both LP and its character in one pass.…”

Section: B the Nature Of Data-driven Alprmentioning

confidence: 99%

License Plate Detection Using Convolutional Neural Network–Back to the Basic With Design of Experiments

2022

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Automatic License Plate Recognition (ALPR) is one of the applications that hugely benefited from Convolutional Neural Network (CNN) processing which has become the mainstream processing method for complex data. Many ALPR research proposed new CNN model designs and post-processing methods with various levels of performances in ALPR. However, good performing models such as YOLOv3 and SSD in more general object detection and recognition tasks could be effectively transferred to the license plate detection application with a small effort in model tuning. This paper focuses on the design of experiment (DOE) of training parameters in transferring YOLOv3 model design and optimising the training specifically for license plate detection tasks. The parameters are categorised to reduce the DOE run requirements while gaining insights on the YOLOv3 parameter interactions other than seeking optimized train settings. The result shows that the DOE effectively improve the YOLOv3 model to fit the vehicle license plate detection task.

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“…Deep neural networks (DNNs) have been widely adopted in various fields, such as in image and character recognition and object detection [1][2][3][4][5][6][7][8][9][10]. Driven by the increasing popularity of embedded systems, such as smartphones, active research is being conducted to explore on-device deep learning in embedded systems [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

CitiusSynapse: A Deep Learning Framework for Embedded Systems

2021

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As embedded systems, such as smartphones with limited resources, have become increasingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that of standard PCs. The proposed framework supports an OpenCL-based accelerator engine for accelerator deep learning operations in various embedded systems. Moreover, the parallel processing performance of OpenCL is maximized through an OpenCL kernel that is optimized for embedded GPUs, and the structural characteristics of embedded systems, such as unified memory. Furthermore, an on-device optimizer for optimizing the performance in on-device environments, and model converters for compatibility with conventional frameworks, are provided. The results of a performance evaluation show that the proposed on-device framework outperformed conventional methods.

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Temporal matching prior network for vehicle license plate detection and recognition in videos

Cited by 12 publications

References 13 publications

Devising a method for processing the image of a vehicle's license plate when shooting with a smartphone camera

Devising a method for processing the image of a vehicle's license plate when shooting with a smartphone camera

License Plate Detection Using Convolutional Neural Network–Back to the Basic With Design of Experiments

CitiusSynapse: A Deep Learning Framework for Embedded Systems

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