research-article

Learning Occlusion Disentanglement with Fine-grained Localization for Occluded Person Re-identification

Authors:

Rongrong JiAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 6462 - 6471

https://doi.org/10.1145/3581783.3612452

Published: 27 October 2023 Publication History

Abstract

Person re-identification (Re-ID) has been extensively investigated in recent years. However, many existing paradigms rely on holistic person regions for matching, disregarding the challenges posed by occlusions in real-world scenarios. Recent methods have explored occlusion augmentation or external semantic cues. Nevertheless, these approaches tend to be coarse-grained, discarding valuable semantic information in local regions when determining them as occlusions. In this paper, we propose a Fine-grained Occlusion Disentanglement Network (FODN) that can extract more information from limited person regions. Specifically, we propose a fine-grained occlusion augmentation scheme to generate diverse occlusion data and employ bilinear interpolation and downsampling strategies to obtain fine-grained occlusion labels. We then design an occlusion feature disentanglement Module that decouples norm and angle from features and supervises the occlusion-aware task using the aforementioned occlusion labeling and person re-identification tasks, respectively, resulting in more robust features. Additionally, we propose a dynamic local weight controller to balance the relative importance of various human body parts, thereby improving the model's ability to mine more effective local features from limited human body regions after occlusion removal. Comprehensive experiments on various person Re-ID benchmarks demonstrate the superiority of FODN over state-of-the-art methods.

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Cited By

Chen ZGe Y(2024)Part-Attention Based Model Make Occluded Person Re-Identification Stronger2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650499(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650499
Wang QHuang ZFan HFu STang Y(2024)Unsupervised person re‐identification based on adaptive information supplementation and foreground enhancementIET Image Processing10.1049/ipr2.1327718:14(4680-4694)Online publication date: 18-Nov-2024
https://doi.org/10.1049/ipr2.13277
Zhao YZhu S(2024)Occluded pedestrian re-identification via Res-ViT double-branch hybrid networkMultimedia Systems10.1007/s00530-023-01235-230:1Online publication date: 12-Jan-2024
https://doi.org/10.1007/s00530-023-01235-2

Index Terms

Learning Occlusion Disentanglement with Fine-grained Localization for Occluded Person Re-identification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual content-based indexing and retrieval

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 ACM.

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Published: 27 October 2023

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the National Science Fund for Distinguished Young Scholars
the Natural Science Foundation of Fujian Province of China
National Key R&D Program of China
the National Natural Science Foundation of China

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MM '23

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SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Chen ZGe Y(2024)Part-Attention Based Model Make Occluded Person Re-Identification Stronger2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650499(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650499
Wang QHuang ZFan HFu STang Y(2024)Unsupervised person re‐identification based on adaptive information supplementation and foreground enhancementIET Image Processing10.1049/ipr2.1327718:14(4680-4694)Online publication date: 18-Nov-2024
https://doi.org/10.1049/ipr2.13277
Zhao YZhu S(2024)Occluded pedestrian re-identification via Res-ViT double-branch hybrid networkMultimedia Systems10.1007/s00530-023-01235-230:1Online publication date: 12-Jan-2024
https://doi.org/10.1007/s00530-023-01235-2

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