Vehicle re-identification is the task of identifying the same vehicle in different environments and from different angles and cameras. It is more challenging than re-identification of humans: 1)small differences between vehicles of the same model make it difficult to capture their subtle characteristics; 2)vehicles of different types and colors may have similar characteristics from different viewpoints or external conditions. To address these challenges, we propose a TVG-ReID network, using a Transformer network to enhance features extracted from a CNN backbone network. A vehicle knowledge graph transfer method(Vehicle-Graph) is proposed, which treats each vehicle as a node in a graph, where simple information is transmitted through edges to constrain the distance of the nodes in a metric learning manner. Experiments on two vehicle re-identification datasets demonstrate the good performance of our proposed model.


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    Titel :

    TVG-ReID: Transformer-Based Vehicle-Graph Re-Identification


    Beteiligte:
    Li, Zhiwei (Autor:in) / Zhang, Xinyu (Autor:in) / Tian, Chi (Autor:in) / Gao, Xin (Autor:in) / Gong, Yan (Autor:in) / Wu, Jiani (Autor:in) / Zhang, Guoying (Autor:in) / Li, Jun (Autor:in) / Liu, Huaping (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2023


    Format / Umfang :

    4284845 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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