Traffic-sign recognition (TSR) is an essential component of a driver assistance system (DAS), providing drivers with safety and precaution information. In this paper, we evaluate the performance of k-d trees, random forests, and support vector machines (SVMs) for traffic-sign classification using different-sized histogram-of-oriented-gradient (HOG) descriptors and distance transforms (DTs). We also use the Fisher's criterion and random forests for the feature selection to reduce the memory requirements and enhance the performance. We use the German Traffic Sign Recognition Benchmark (GTSRB) data set containing 43 classes and more than 50 000 images.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-Time Traffic-Sign Recognition Using Tree Classifiers


    Beteiligte:


    Erscheinungsdatum :

    01.12.2012


    Format / Umfang :

    808139 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A real-time traffic sign recognition system

    Estable, S. / Schick, J. / Stein, F. et al. | IEEE | 1994


    A Real-Time Traffic Sign Recognition System

    Estable, S. / Schick, J. / Stein, F. et al. | British Library Conference Proceedings | 1994


    Real-Time Traffic Sign Recognition Using Convolutional Neural Networks

    Rao, Aditya / Motwani, Rahul / Sarguroh, Naveed et al. | Springer Verlag | 2021


    Attention-based traffic sign recognition with an array of weak classifiers

    Kastner, Robert / Michalke, Thomas / Burbach, Thomas et al. | IEEE | 2010


    A Real-Time Malaysian Traffic Sign Recognition Using YOLO Algorithm

    Mangshor, Nur Nabilah Abu / Paudzi, Nurul Paudziah Aida Mohd / Ibrahim, Shafaf et al. | Springer Verlag | 2021