As a significant topic in Intelligent Transportation Systems (ITS), vehicle Re-Identification (Re-ID) has attracted increasing research attention. However, the variation of shooting scenes and the similar appearance among the vehicles with the same type and color lead to large intra-class variances and small inter-class variances, respectively. To address the problems, we propose a novel Mask-Aware Reasoning Transformer (MART) to extract the background-unrelated global features and perspective-invariant local features. The MART contains three effective modules including Foreground Global Features Extraction (FGFE), Mask-guided Local Features Extraction (MLFE) and Cross-images Local Features Reasoning (CLFR). Firstly, due to the complexity of background information in images, identity representation inevitably contains background elements, which may impact identity matching. To address this issue, we propose the FGFE to extract background-independent global features by introducing the mask semantic information to the inputs of Vision Transformer (ViT). Secondly, to fill the gap that the previous local features extraction methods cannot be directly applied to ViT, the MLFE is presented to extract distinctive local features by recombining token features according to vehicle mask. Thirdly, when local components are invisible in the image due to the occlusion problem, the corresponding local information is absent from the image, leading to unreliable local features. To solve this problem, the CLFR is proposed to reason the occluded local features by exploiting the correlation between cross-image local features. We carry out comprehensive experiments to illustrate the effectiveness of the MART on two challenging datasets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    MART: Mask-Aware Reasoning Transformer for Vehicle Re-Identification


    Beteiligte:
    Lu, Zefeng (Autor:in) / Lin, Ronghao (Autor:in) / Hu, Haifeng (Autor:in)


    Erscheinungsdatum :

    01.02.2023


    Format / Umfang :

    3862411 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Mask-Aware Pseudo Label Denoising for Unsupervised Vehicle Re-Identification

    Lu, Zefeng / Lin, Ronghao / He, Qiaolin et al. | IEEE | 2023


    MsKAT: Multi-Scale Knowledge-Aware Transformer for Vehicle Re-Identification

    Li, Hongchao / Li, Chenglong / Zheng, Aihua et al. | IEEE | 2022


    Plant Mart

    Online Contents | 2008


    Product Mart

    Online Contents | 1998


    Product Mart

    Online Contents | 1996