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Semantically Enhanced Attention Map-Driven Occluded Person Re-identification
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  • Yiyuan Ge,
  • Mingxin Yu,
  • Zhihao Chen,
  • Wenshuai Lu,
  • Huiyu Shi
Yiyuan Ge
Beijing Information Science and Technology University
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Mingxin Yu
Beijing Information Science and Technology University

Corresponding Author:[email protected]

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Zhihao Chen
Beijing Information Science and Technology University
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Wenshuai Lu
Tsinghua University
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Huiyu Shi
Tsinghua University
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Abstract

Occluded person Re-identification (Re-ID) is to identify a particular person when the person’s body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. In this paper, we propose a novel Attention Map-Driven Network (AMD-Net) for occluded person Re-ID. In AMD-Net, human parsing labels are introduced to supervise the generation of partial attention maps, while we suggest a Spatial-frequency Interaction Module (SIM) to complement the higher-order semantic information from the frequency domain. Furthermore, we propose a Taylor-inspired Feature Filter (TFF) for mitigating background disturbance and extracting fine-grained features. Moreover, we also design a part-soft triplet loss, which is robust to non-discriminative body partial features. Experimental results on Occluded-Duke, Occluded-Reid, Market-1501, and Duke-MTMC datasets show that our method outperforms existing state-of-the-art methods. The code is available at: https://github.com/ISCLab-Bistu/SA-ReID.
23 Feb 2024Submitted to Electronics Letters
23 Feb 2024Review(s) Completed, Editorial Evaluation Pending
02 Mar 2024Reviewer(s) Assigned
18 Mar 2024Editorial Decision: Revise Major
08 Apr 20241st Revision Received
11 Apr 2024Submission Checks Completed
11 Apr 2024Assigned to Editor
11 Apr 2024Review(s) Completed, Editorial Evaluation Pending
30 Apr 2024Editorial Decision: Accept