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
2019-02-01
11 pages
Article (Journal)
Electronic Resource
English
statistical analysis , denoising approach , road safety , object detection , traffic signs identification , road traffic , estimated threshold , traffic engineering computing , correlational property , blue traffic signs , morphological operations , enhancement procedure , image classification , SVM‐KNN classifier , image segmentation , road awareness , statistical measures , digital recognition , image filtering , weighted fusion , image colour analysis , support vector machine‐K‐nearest neighbour classifier , nonpotential regions , object recognition , GTSRB data set , feature vector , red traffic signs , digital segmentation , histogram‐of‐oriented gradient features , nearest neighbour methods , support vector machines , image enhancement , traffic sign detection , global threshold value , image fusion , automatic traffic sign measurement
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