This paper describes a traffic sign detection (TSD) framework that is capable of rapidly detecting multiclass traffic signs in high-resolution images while achieving a high detection rate. There are three key contributions. The first is the introduction of two features called multiblock normalization local binary pattern (MN-LBP) and tilted MN-LBP (TMN-LBP), which are able to express multiclass traffic signs effectively. The second is a tree structure called split-flow cascade, which utilizes common features of multiclass traffic signs to construct a coarse-to-fine TSD detector. The third contribution is the Common-Finder AdaBoost (CF.AdaBoost) algorithm, which is designed to find common features of different training sets to develop an efficient Split-Flow Cascade tree (SFC-tree) for multiclass TSD. Through experiments with an evaluation data set of high-resolution images, we show that the proposed framework is able to detect multiclass traffic signs with high detection accuracy in real time and that it outperforms the state-of-the-art approaches at detecting a large number of different types of traffic signs rapidly without using any color information.


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    Titel :

    Rapid Multiclass Traffic Sign Detection in High-Resolution Images


    Beteiligte:
    Liu, Chunsheng (Autor:in) / Chang, Faliang (Autor:in) / Chen, Zhenxue (Autor:in)


    Erscheinungsdatum :

    01.12.2014


    Format / Umfang :

    877055 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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