Real-time Traffic Sign Recognition system with deep convolutional neural network

Jung, Seokwoo; Lee, Unghui; Jung, Jae-Seung; Shim, David Hyunchul

doi:10.1109/urai.2016.7734014

Cited by 44 publications

(27 citation statements)

References 6 publications

(4 reference statements)

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“…Evaluation of performance based on detection rate is done with the video sequences. Jung [2] used an artificial neural network algorithm for training and the classification. The present a traffic sign recognition system in real time with deep convolution that helped in advanced driver assistance to recognize the traffic sign board for more information on roads.…”

Section: Stopmentioning

confidence: 99%

Traffic Sign Board Detection and Recognition for Autonomous Vehicles and Driver Assistance Systems

Chincholkar¹,

Kumar²

2019

IJIVP

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In the recent year's many approaches have been made that uses image processing algorithms to detect traffic sign boards. Edge detection is used to avoid segmentation problems of the existing method. Color based segmentation faces the challenge of adaptive thresholding which fails in real time scenarios. This proposed algorithm is yet another approach to detect traffic sign boards from video sequences. The first step of this work is the pre-processing of the video frame which is achieved by the gray scale conversion and edge detection and the second step is the extraction of the objects. Hough Transform algorithm is then applied to measure properties of image regions for further analysis. The different feature points which include perimeter, area, filled area, solidity and centroid are extracted for the detection of the traffic sign board. Feature generation and classification are done on the recognition side to get the class of the detected object. The input for the project is video sequences taken from a camera placed on the vehicle.

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Section: Stopmentioning

confidence: 99%

Traffic Sign Board Detection and Recognition for Autonomous Vehicles and Driver Assistance Systems

Chincholkar¹,

Kumar²

2019

IJIVP

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“…One of the currently followed frameworks is deep learning based framework such as Convolutional Neural Network (CNN) Ref. [29,30,31].CNN pioneered the way parallel programming with high-end GPU which provides high efficiency in real time application Ref. [29].In the field of Bengali based TSDR progress is only little.…”

Section: Analysis Aspects Of Traffic Sign Detection and Recognitionmentioning

confidence: 99%

“…[29,30,31].CNN pioneered the way parallel programming with high-end GPU which provides high efficiency in real time application Ref. [29].In the field of Bengali based TSDR progress is only little. The existing state of earth frameworks is not feasible for real-time implementation.…”

Section: Analysis Aspects Of Traffic Sign Detection and Recognitionmentioning

confidence: 99%

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Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Nandi¹,

Saif²,

Paul³

et al. 2018

IJMECS

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Automated Vehicular System has become a necessity in the current technological revolution. Real Traffic sign detection and recognition is a vital part of that system that will find roadside traffic signs to warn the automated system or driver beforehand of the physical conditions of roads. Mostly, researchers based on Traffic sign detection face problems such as locating the sign, classifying it and distinguishing one sign from another. The most common approach for locating and detecting traffic signs is the color information extraction method. The accuracy of color information extraction is dependent upon the selection of a proper color space and its capability to be robust enough to provide color analysis data. Techniques ranging from template matching to critical Machine Learning algorithms are used in the recognition process. The main purpose of this research is to give a review based on methods and framework of Traffic Sign Detection and Recognition solution and discuss also the current challenges of the whole solution.

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“…This support vectors [17] would be the facts factors that will be better to the taking away hyperplane; these varieties of factors usually are within the boundary from the slab. The next determine demonstrates these varieties of definitions, along with + showing facts factors regarding form 1, in addition to -showing facts factors regarding form -1.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Recognition and Classification of Traffic Signs using Machine Learning Techniques

Rai¹,

Pachlasiya²

2017

IJCA

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The computerized recognition and classification of traffic signs is a challenging problem, with several important request areas, including advanced drivers assistance systems, autonomous vehicles and street surveying. While much research is present on both automated diagnosis and popularity of symbol-based traffic indicators there is much less research concentrated specifically on the reputation of wording on traffic information indications. This may be partial because of the difficulty of the duty brought on by problems, such as brightness and shadows, blurring, occlusion, and signal deterioration. Our method of this issue by detecting many text-based traffic indication prospects using basic condition and color information. The proposed system includes two main periods: Recognition and Classification. The Acceptance stage exploits the understanding of the composition of the Traffic indication, i.e., the condition and size of the sign in the frame, to look for the locations in the landscape that it will seek out traffic text indications.

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Real-time Traffic Sign Recognition system with deep convolutional neural network

Cited by 44 publications

References 6 publications

Traffic Sign Board Detection and Recognition for Autonomous Vehicles and Driver Assistance Systems

Traffic Sign Board Detection and Recognition for Autonomous Vehicles and Driver Assistance Systems

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Recognition and Classification of Traffic Signs using Machine Learning Techniques

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