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.
Automatic measurement of the traffic sign with digital segmentation and recognition
IET Intelligent Transport Systems ; 13 , 2 ; 269-279
2018-10-24
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
red traffic signs , image fusion , digital recognition , road awareness , nonpotential regions , image filtering , nearest neighbour methods , image enhancement , support vector machine-K-nearest neighbour classifier , digital segmentation , traffic sign detection , feature vector , traffic signs identification , global threshold value , SVM-KNN classifier , road traffic , automatic traffic sign measurement , image segmentation , weighted fusion , image colour analysis , histogram-of-oriented gradient features , object detection , image classification , road safety , denoising approach , statistical analysis , correlational property , estimated threshold , morphological operations , blue traffic signs , support vector machines , enhancement procedure , traffic engineering computing , object recognition , statistical measures , GTSRB data set
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