Abstract Using unmanned aerial vehicles (UAV) as devices for traffic data collection exhibits many advantages in collecting traffic information. This paper introduces a new vehicle dataset based on image data collected by UAV first. Then a novel learning framework for robust on-road vehicle recognition is presented. This framework starts with conventional supervised learning to create initial training data set. Then a tracking-based online learning approach is applied on consecutive frames to improve the accuracy of vehicle recogniser. Experimental results show that the proposed algorithm exhibits high accuracy in vehicle recognition at different UAV altitudes with different view scopes, which can be used in future traffic monitoring and control in metropolitan areas.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Detection and Classification of Vehicle Types from Moving Backgrounds


    Beteiligte:
    Le, Xuesong (Autor:in) / Jo, Jun (Autor:in) / Youngbo, Sakong (Autor:in) / Stantic, Dejan (Autor:in)


    Erscheinungsdatum :

    31.05.2018


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Detection and Classification of Vehicle Types from Moving Backgrounds

    Le, Xuesong / Jo, Jun / Youngbo, Sakong et al. | TIBKAT | 2019


    Improving Learning Effectiveness For Object Detection and Classification in Cluttered Backgrounds

    Varatharasan, Vinorth / Shin, Hyo-Sang / Tsourdos, Antonios et al. | IEEE | 2019


    Moving object detection in the presence of dynamic backgrounds using intensity and textural features

    Chiranjeevi, P. / Sengupta, S. | British Library Online Contents | 2011



    Selected Aspects of Motor Vehicle Research Backgrounds

    Kisilowski, Jerzy / Zalewski, Jarosław | Springer Verlag | 2022