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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    Title :

    Accurate Object Detection in Smart Transportation Using Multiple Cameras


    Contributors:
    Qiao, Zhinan (author) / Sansom, Andrew (author) / McGuire, Mara (author) / Kalaani, Andrew (author) / Ma, Xu (author) / Yang, Qing (author) / Fu, Song (author)


    Publication date :

    2020-02-01


    Size :

    678523 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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