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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    Title :

    Rapid Multiclass Traffic Sign Detection in High-Resolution Images


    Contributors:


    Publication date :

    2014-12-01


    Size :

    877055 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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