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Cross-Modality Person Re-Identification Method with Joint-Modality Generation and Feature Enhancement.

Yihan Bi1, Rong Wang1,2, Qianli Zhou3

  • 1School of Information and Cyber Security, People's Public Security University of China, Beijing 100038, China.

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Summary

This study introduces a novel cross-modality person re-identification method using modality generation and feature enhancement. The approach significantly improves accuracy in visible and infrared pedestrian recognition tasks.

Keywords:
feature enhancementgradient centralizationmodality generationperson re-identificationvisible–infrared image

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Visible and infrared (IR) pedestrian re-identification (re-ID) faces challenges due to modality disparity.
  • Effective feature representation across different sensors is crucial for accurate re-ID.

Purpose of the Study:

  • To propose a cross-modality person re-identification method that minimizes visible-IR disparity.
  • To enhance pedestrian feature representation for improved re-ID performance.

Main Methods:

  • A lightweight network for visible image dimension reduction and augmentation to generate intermediate modalities.
  • Integration of Convolutional Block Attention Module (CBAM) into ResNet50 for enhanced feature emphasis.
  • Incorporation of Gradient Centralization into Stochastic Gradient Descent (SGD) optimizer for faster convergence and better generalization.

Main Results:

  • Significant performance gains on SYSU-MM01 and RegDB datasets.
  • Rank-1 accuracy increased by 7.12% and 6.34% respectively.
  • Mean Average Precision (mAP) improved by 4.00% and 6.05% respectively.

Conclusions:

  • The proposed method effectively bridges the gap between visible and infrared modalities.
  • The integration of CBAM and Gradient Centralization enhances network performance for cross-modality re-ID.
  • The approach demonstrates superior results in pedestrian re-identification across diverse datasets.