Automatic measurement of the traffic sign with digital segmentation and recognition. Issue 2 (24th October 2018)

• Record Type: Journal Article
• Authors: Khalid, Sara; Muhammad, Nazeer; Sharif, Muhammad
• Is Part Of: IET intelligent transport systems. Volume 13:Issue 2(2019)
• Page Start: 269
• Page End: 279
• Publication Date: 2018-10-24
• Subjects: road traffic -- traffic engineering computing -- object detection -- object recognition -- image segmentation -- image fusion -- image enhancement -- image colour analysis -- image filtering -- image classification -- nearest neighbour methods -- support vector machines -- statistical analysis; automatic traffic sign measurement -- digital segmentation -- digital recognition -- traffic sign detection -- road awareness -- road safety -- traffic signs identification -- global threshold value -- correlational property -- red traffic signs -- blue traffic signs -- estimated threshold -- morphological operations -- enhancement procedure -- statistical measures -- nonpotential regions -- histogram‐of‐oriented gradient features -- support vector machine‐K‐nearest neighbour classifier -- SVM‐KNN classifier -- denoising approach -- weighted fusion -- GTSRB data set -- feature vector; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2018.5223
• Languages: English
• ISSNs: 1751-956X

Abstract

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.