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Improving Person Re-identification by Mask Guiding and Part Pooling

Published: 26 May 2020 Publication History

Abstract

Person re-identification (re-ID) is a promising computer vision task. State-of-the-art methods mainly utilize deep learning based approaches to learn visual features for describing person appearances. Due to occlusion, complex background, different postures and light intensity, the technology faces many challenges. In this paper, a person re-identification method is proposed combining mask guiding and part pooling. First, person mask is generated by a segmentation sub-net, combining Macro-Micro Adversarial Network (MMAN) with Fully Convolution Networks (FCN). To alleviate the influence of background, a mask guiding strategy is designed integrating the mask with person feature map. Then a part pooling strategy is employed to extract local features of the person. The final loss function of the network is defined as the combination of global loss and local loss, which can describe the person in a comprehensive manner. Four public datasets are employed to test the performance of the proposed method. Our method achieves rank-1/mAP of 89.05%/72.83% on the Market-1501, 79.03%/62.06% on the DukeMTMC-reID, 46.79%/29.61% on the MSMT-17, 49.50%/45.03% on the CUHK03-NP. Experimental results show that the designed mask guiding and part pooling strategies can improve person re-ID performance.

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Cited By

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  • (2024)A Study of Occluded Person Re-Identification for Shared Feature Fusion with Pose-Guided and Unsupervised Semantic SegmentationElectronics10.3390/electronics1322452313:22(4523)Online publication date: 18-Nov-2024
  • (2024)Person Re-Identification Network Based on Edge-Enhanced Feature Extraction and Inter-Part Relationship ModelingApplied Sciences10.3390/app1418824414:18(8244)Online publication date: 13-Sep-2024
  • (2023)PMA-Net: A parallelly mixed attention network for person re-identificationDisplays10.1016/j.displa.2023.10243778(102437)Online publication date: Jul-2023

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    cover image ACM Other conferences
    ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing
    February 2020
    607 pages
    ISBN:9781450376426
    DOI:10.1145/3383972
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Shenzhen University: Shenzhen University

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    Published: 26 May 2020

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    Author Tags

    1. Mask feature fusion
    2. Part pooling
    3. Person re-ID
    4. Person segmentation

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    • (2024)A Study of Occluded Person Re-Identification for Shared Feature Fusion with Pose-Guided and Unsupervised Semantic SegmentationElectronics10.3390/electronics1322452313:22(4523)Online publication date: 18-Nov-2024
    • (2024)Person Re-Identification Network Based on Edge-Enhanced Feature Extraction and Inter-Part Relationship ModelingApplied Sciences10.3390/app1418824414:18(8244)Online publication date: 13-Sep-2024
    • (2023)PMA-Net: A parallelly mixed attention network for person re-identificationDisplays10.1016/j.displa.2023.10243778(102437)Online publication date: Jul-2023

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