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Video-based person re-identification with complementary local and global features using a graph transformer.

Hai Lu1, Enbo Luo1, Yong Feng1

  • 1Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650217, China.

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|August 23, 2024
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Summary
This summary is machine-generated.

This study introduces a graph transformer model to improve video person re-identification (Re-ID) by capturing relationships between local regions. The novel approach enhances feature representation for more accurate person matching in videos.

Keywords:
graphperson re-identificationtransformervideo

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video-based person re-identification (Re-ID) is crucial for surveillance and security.
  • Current Re-ID methods struggle with robust feature representation, especially by overlooking local region correlations.
  • Extracting discriminative and robust person features remains a key challenge in video Re-ID.

Purpose of the Study:

  • To propose a novel representation learning approach for video person Re-ID.
  • To effectively model and leverage the correlations between local regions within video frames.
  • To enhance the discriminative power and robustness of person features for improved Re-ID accuracy.

Main Methods:

  • A graph transformer is employed to model relationships between local regions, facilitating contextual feature interactions.
  • A local relation graph is constructed to represent intrinsic relationships between local regions (nodes).
  • A vision transformer-based global feature learning branch captures inter-frame relationships, integrated via a dual-branch interaction network with multi-head fusion attention.

Main Results:

  • The proposed graph transformer approach effectively models relationships between local regions.
  • Integration of local and global features through a dual-branch network enhances representation learning.
  • Experimental results on iLIDS-VID, MARS, and DukeMTMC-VideoReID datasets demonstrate competitive performance.

Conclusions:

  • The novel graph transformer-based approach significantly improves video person Re-ID by capturing inter-region correlations.
  • The dual-branch network effectively fuses local and global features for robust person representation.
  • The method validates the effectiveness of leveraging relational information for enhanced video person Re-ID performance.