Vehicle re-identification (ReID) across multiple cameras is one of the principal issues in Intelligent Transportation System (ITS). The main challenge that vehicle ReID presents is the large intra-class and small inter-class variability of vehicles appearance, followed by illumination changes, different viewpoints and scales, lack of labelled data and camera resolution. To address these problems, we present a vehicle ReID system that combines different ReID models, including appearance and orientation deep learning features. Additionally, for results refinement re-ranking and a post-processing step taking into account the vehicle trajectory information provided by the CityFlow-ReID dataset are applied.


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    Titel :

    Vehicle Re-Identification in Multi-Camera scenarios based on Ensembling Deep Learning Features


    Beteiligte:


    Erscheinungsdatum :

    01.06.2020


    Format / Umfang :

    1633600 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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