Recently, more and more attention has been paid to the connected object detection for better performance. One of the most interesting fields is learning from multiple resources in a connected fashion. In this paper, we present a connected object detection method using multiple cameras for the smart transportation system. The proposed architecture consists of three parts: an alignment framework, a deep multi-view fusion network and an object detection network. Experiments are conducted to illustrate the performance of our proposed architecture.


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    Titel :

    Accurate Object Detection in Smart Transportation Using Multiple Cameras


    Beteiligte:
    Qiao, Zhinan (Autor:in) / Sansom, Andrew (Autor:in) / McGuire, Mara (Autor:in) / Kalaani, Andrew (Autor:in) / Ma, Xu (Autor:in) / Yang, Qing (Autor:in) / Fu, Song (Autor:in)


    Erscheinungsdatum :

    01.02.2020


    Format / Umfang :

    678523 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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