This paper present an efficient real time rectangle speed limit sign recognition system. The system design considers computation load and hardware resources for driver assistant system. First multi-scale overlapping LBP features are used to train AdaBoost cascade classifier for speed limit sign object detection. Then a simple linear prediction method is used to do tracking task. At the recognition stage, a novel efficient algorithm is used to correct rotation angle, and then integral image based adaptive threshold algorithm is adopted to segment the speed limit number. The clustering based binary tree of linear support vector machine is adopted for classification task. The system is tested on real road scene video sequences. It achieves 98.3% recognition rate with approximate 16 fps frame rate on laptop.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An efficient real time rectangle speed limit sign recognition system


    Beteiligte:
    Zhang Yankun, (Autor:in) / Hong Chuyang, (Autor:in) / Wang, Charles (Autor:in)


    Erscheinungsdatum :

    01.06.2010


    Format / Umfang :

    1739548 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Efficient Real Time Rectangle Speed Limit Sign Recognition System, pp. 34-38

    Zhang, Y. / Hong, C. / Charles, W. et al. | British Library Conference Proceedings | 2010


    FPGA versus GPU for Speed-Limit-Sign Recognition

    Yih, Matthew / Ota, Jeffrey M. / Owens, John D. et al. | IEEE | 2018


    TRAFFIC SIGN RECOGNITION SYSTEM AND METHOD REFLECTING CONDITIONAL SPEED LIMIT

    RO DAI CHANG | Europäisches Patentamt | 2019

    Freier Zugriff

    An Efficient Real-Time Speed Limit Signs Recognition Based on Rotation Invariant Feature

    Liu, W. / Lv, J. / Gao, H. et al. | British Library Conference Proceedings | 2011