Article

Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians

Authors:

Xing SunAuthors Info & Claims

Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI

Pages 647 - 663

https://doi.org/10.1007/978-3-030-58539-6_39

Published: 23 August 2020 Publication History

Abstract

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods.

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Cited By

Liu ZLin YLiu TDu B(2023)Reliable Cross-Camera Learning in Random Camera Person Re-IdentificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.334187734:6(4556-4567)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3341877
Tang ZHuang J(2022)Harmonious Multi-branch Network for Person Re-identification with Harder Triplet LossACM Transactions on Multimedia Computing, Communications, and Applications10.1145/350140518:4(1-21)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3501405

Index Terms

Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Guide Proceedings

Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI

Aug 2020

835 pages

ISBN:978-3-030-58538-9

DOI:10.1007/978-3-030-58539-6

Editors:
Andrea Vedaldi
University of Oxford, Oxford, UK
,
Horst Bischof
Graz University of Technology, Graz, Austria
,
Thomas Brox
University of Freiburg, Freiburg im Breisgau, Germany
,
Jan-Michael Frahm
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 August 2020

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Cited By

Liu ZLin YLiu TDu B(2023)Reliable Cross-Camera Learning in Random Camera Person Re-IdentificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.334187734:6(4556-4567)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3341877
Tang ZHuang J(2022)Harmonious Multi-branch Network for Person Re-identification with Harder Triplet LossACM Transactions on Multimedia Computing, Communications, and Applications10.1145/350140518:4(1-21)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3501405

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