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Overview of deep learning based pedestrian attribute recognition and re-identification.

Duidi Wu1, Haiqing Huang1, Qianyou Zhao1

  • 1Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.

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Summary
This summary is machine-generated.

This review explores deep learning for pedestrian re-identification (ReID) and its link with pedestrian attribute recognition (PAR). It covers methods, challenges like cloth-changing, and future directions for intelligent surveillance.

Keywords:
Computer visionPedestrian attribute recognitionPerson re-identification

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Pedestrian attribute recognition (PAR) and re-identification (ReID) are crucial for intelligent surveillance and smart city applications.
  • Deep learning has significantly advanced these fields, necessitating a comprehensive review of current methodologies.

Purpose of the Study:

  • To review deep learning-based ReID methods.
  • To analyze the relationship and differences between PAR and ReID.
  • To provide insights into attribute-assisted ReID.

Main Methods:

  • Summarizing key concepts in attribute-assisted ReID.
  • Comparing datasets and algorithmic challenges in PAR and ReID.
  • Introducing representative deep learning ReID methods, detailing network structures, loss functions, and training strategies.

Main Results:

  • Analysis of cutting-edge ReID research, including solutions for cloth-changing, domain adaptation, occlusion, and resolution variations.
  • Evaluation of state-of-the-art (SOTA) ReID methods' performance and characteristics.

Conclusions:

  • Attribute-assisted ReID shows significant effectiveness.
  • The review provides a foundation for future research in ReID and PAR for enhanced intelligent surveillance systems.