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Image-to-image domain adaptation for vehicle re-identification

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Abstract

Cross-domain vehicle re-identification (ReID) is an interesting but challenging task in computer vision. A ReID model well-trained on one dataset often experiences a severe performance drop when applied to another dataset due to the domain discrepancy between the different datasets. This is especially true for low-resolution images. In this paper, we present a vehicle image domain adaptation framework (VDAF) which contains a single-image super resolution network (SISR) and a vehicle transfer generative adversarial network (VTGAN). SISR is an enhancement task for mapping low-resolution (LR) images to high-resolution (HR) images. Based on the reconstructed HR images, VTGAN can translate vehicle images from a source domain to a target domain with consistent styles and identities. VTGAN is an unsupervised approach designed for source-target translation for vehicle ReID and is composed of two adversarial networks and one Siamese network. Based on the translated images, we can infer an enhanced vehicle representation free of influences from style variations, allowing distance metrics for vehicle ReID to be learned. Through extensive experiments on the VeRi, VehicleID, and VRIC datasets, we show that images translated by VTGAN are effective for domain adaptation and are superior at promoting the accuracy of vehicle ReID.

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Data Availability

The datasets analysed during the current study are available in the [Baidu Netdisk] repository, [Link: https://pan.baidu.com/s/10-dK1SmV4A65V1atK2wIPg, Extraction code: hlqp].

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Funding

This work was supported in part by the Key Research and Development and Promotion in Henan Province (Science and Technology Research) under Grant 222102240045, in part by the Key Scientific Research Projects of Colleges and Universities in Henan Province under Grant 22A520028, in part by the Fundamental Research Funds for the Universities of Henan Province under Grant NSFRF210342.

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Authors and Affiliations

  1. School of Software, Henan Polytechnic University, Henan, 454000, China

    Fukai Zhang, Lulu Zhang & Yongqiang Ma

  2. College of Safety Science and Engineering, Henan Polytechnic University, Henan, 454000, China

    Fukai Zhang

  3. School of Computer Science and Technology, Henan Polytechnic University, Henan, 454000, China

    Haiyan Zhang

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  1. Fukai Zhang
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  2. Lulu Zhang
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Correspondence to Haiyan Zhang.

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Zhang, F., Zhang, L., Zhang, H. et al. Image-to-image domain adaptation for vehicle re-identification. Multimed Tools Appl 82, 40559–40584 (2023). https://doi.org/10.1007/s11042-023-14839-7

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