Traffic sign recognition by combining global and local features based on semi‐supervised classification. Issue 5 (5th December 2019)

• Record Type: Journal Article
• Authors: He, Zhenli; Nan, Fengtao; Li, Xinfa; Lee, Shin‐Jye; Yang, Yun
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 5(2020)
• Page Start: 323
• Page End: 330
• Publication Date: 2019-12-05
• Subjects: object recognition -- traffic engineering computing -- learning (artificial intelligence) -- feature extraction -- image classification -- image fusion -- edge detection -- image colour analysis; IOT‐based transport system -- German Traffic Sign Recognition Benchmark dataset -- global features -- local features -- labelled traffic sign data -- Internet‐of‐things–based transport system -- semisupervised classification -- high‐quality traffic sign recognition model -- histograms of oriented gradient -- colour histograms -- edge features -- fusion feature space; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0409
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : The legibility of traffic signs has been considered from the beginning of design, and traffic signs are easy to identify for humans. For computer systems, however, identifying traffic signs still poses a challenging problem. Both image‐processing and machine‐learning algorithms are constantly improving, aimed at better solving this problem. However, with a dramatic increase in the number of traffic signs, labelling a large amount of training data means high cost. Therefore, how to use a small number of labelled traffic sign data reasonably to build an efficient and high‐quality traffic sign recognition (TSR) model in the Internet‐of‐things–based (IOT‐based) transport system has been an urgent research goal. Here, the authors propose a novel semi‐supervised learning approach combining global and local features for TSR in an IOT‐based transport system. In their approach, histograms of oriented gradient, colour histograms (CH), and edge features (EF) are used to build different feature spaces. Meanwhile, on the unlabelled samples, a fusion feature space is found to alleviate the differences between different feature spaces. Extensive evaluations on a collection of signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset shows that the proposed approach outperforms the others and provides a potential solution for practical applications.