A real-time traffic sign recognition system based on local structure features

Kwangyong Lim, Hyeran Byun, Yeongwoo Choi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We present an accurate and efficient system for traffic sign recognition in a real-world driving scene video. The proposed system uses local structure features to achieve high, illumination-invariant accuracy in detection and recognition. We exploit a property of traffic signs, namely, shared boundary shapes, to enhance the speed and accuracy of the detection step. A multi-level SVM structure is employed for stable recognition. The proposed method can process real-world road driving scene video in real time with high accuracy, over 98%, in both detection and recognition.

    Original languageEnglish
    Title of host publicationProceedings of the 2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015
    EditorsHamid R. Arabnia, Leonidas Deligiannidis, Fernando G. Tinetti, George Jandieri, Gerald Schaefer, Ashu M. G. Solo
    PublisherCSREA Press
    Pages65-68
    Number of pages4
    ISBN (Electronic)1601324049, 9781601324047
    Publication statusPublished - 2015 Jan 1
    Event2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015, at WORLDCOMP 2015 - Las Vegas, United States
    Duration: 2015 Jul 272015 Jul 30

    Publication series

    NameProceedings of the 2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015

    Conference

    Conference2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015, at WORLDCOMP 2015
    Country/TerritoryUnited States
    CityLas Vegas
    Period15/7/2715/7/30

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition
    • Computer Graphics and Computer-Aided Design

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