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MFC-Net: A Multiple Feature Complementation Network for Person Re-identification in Aerial Imagery

Published: 11 August 2023 Publication History

Abstract

Person re-identification on Unmanned Aerial Vehicles (UAVs) platforms has received widespread attention, but visual monitoring on UAVs is affected by pixels, angles, and more misalignment, which impairs the discriminative ability of the learning representation and brings new challenges to person re-identification tasks. In order to solve the problem, we propose a Multiple Feature Complementation Network (MFC-Net). MFC-Net consists of two modules, the Parallel Dual Attention Modules (PDAM) and the Multilayer Feature Fusion Module (MFFM). The PDAM consists of two attention branches—Multiscale Channel Attention (MCA) and Weighted Positional Attention (WPA). The PDAM can effectively perceive regional features and better focus the image. The MFFM further fuses two complementary attention features, which effectively solves the problems of angle and misalignment and improves the accuracy of person re-identification. Compared with existing techniques, MFC-Net performs well in the person re-identification of aerial imagery.

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    ICMIP '23: Proceedings of the 2023 8th International Conference on Multimedia and Image Processing
    April 2023
    131 pages
    ISBN:9781450399586
    DOI:10.1145/3599589
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    Published: 11 August 2023

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    Author Tags

    1. Attention mechanism
    2. Branch Architecture
    3. Person Re-identification
    4. Unmanned Aerial Vehicles (UAVs)

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