NDLI: Automatic measurement of the traffic sign with digital segmentation and recognition

Content Provider	IET Digital Library
Author	Khalid, Sara Muhammad, Nazeer Sharif, Muhammad
Abstract	Traffic sign detection assists in driving by acquiring the temporal and spatial information of the potential signs for road awareness and safety. The purpose of conducting research on this topic is introduced to a novel and less complex algorithm that works for traffic signs identification, accurately. Initially, the authors estimate the global threshold value using the correlational property of the given image. In order to get red and blue traffic signs, a segmentation algorithm is developed using estimated threshold and morphological operations followed by an enhancement procedure, the net outcome of which is provided the greater number of potential signs. Moreover, remaining regions are filtered in terms of statistical measures using the non-potential regions. Furthermore, detection is performed on the basis of histogram of oriented gradient features by employing the support vector machine (SVM)–K-nearest neighbour (KNN) classifier. The denoising approach with the weighted fusion of KNN and SVM is used in order to improve the performance of the proposed algorithm by reducing the false positive. A recognition phase is performed on the GTSRB data set in order to formulate the feature vector. The proposed method performed the significant recognition with an accuracy rate of 99.32%. It is quite comparable to the existing state-of-the-art techniques.
Starting Page	269
Ending Page	279
Page Count	11
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5223
Journal	IET Intelligent Transport Systems
Publisher Date	2018-09-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Traffic Sign Measurement Blue Traffic Signs Computer Vision And Image Processing Technique Correlational Property Denoising Approach Digital Recognition Digital Segmentation Enhancement Procedure Estimated Threshold Feature Vector Filtering Method in Signal Processing Global Threshold Value GTSRB Data Set Histogram-of-oriented Gradient Feature Image Classification Image Colour Analysis Image Enhancement Image Filtering Image Fusion Image Recognition Image Segmentation Knowledge Engineering Technique Morphological Operation Nearest Neighbour Method Nonpotential Region Object Detection Object Recognition Red Traffic Signs Road Awareness Road Safety Road Traffic Sensor Fusion Statistical Analysis Statistical Measures Statistics Support Vector Machine Support Vector Machine-K-nearest Neighbour Classifier SVM-KNN Classifier Traffic Engineering Computing Traffic Sign Detection Traffic Signs Identification Weighted Fusion
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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