Evaluation of person re-identification methods based on four architectures of the SBS family

Хамдамов, Р.Х., Рахманов, Х.Э., Раматов, И.И.

Рақамли технологияларнинг назарий ва амалий масалалари · 2026-yil

Annotatsiya

This paper considers the problem of person re-identification (Re-ID), which plays a key role in intelligent video surveillance and behavior analysis systems. The main focus is on the comparative analysis of methods implemented based on the Strong Baseline System (SBS), the basic neural network architecture for solving the Re-ID problem. The study analyzes different model variants: SBS(R50), which uses ResNet-50 as a base network; SBS(R50-IBN), which applies Instance-Batch Normalization to improve robustness to image style variations; SBS(S50), built on the basis of SE-ResNet-50 with a feature enhancement mechanism; and SBS(R101-IBN), which combines the deeper architecture of ResNet-101 with IBN to achieve maximum accuracy when working with large datasets. The analysis reveals the strengths and weaknesses of each SBS modification: from the high speed and stability of the base version (R50) to improved generalization ability (R50-IBN), enhanced discriminative features (S50), and high accuracy at high computational costs (R101-IBN). The results confirm that the choice of a specific model should depend on system requirements-speed, domain-shift tolerance, or a priority for maximum accuracy.

Maqola ma’lumotlari
MualliflarХамдамов, Р.Х., Рахманов, Х.Э., Раматов, И.И.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2026-03-02
Jild9
Son1
Betlar49-56
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v9i1.322
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v9i1.322 · Maqolaning asl sahifasi

Kalit so‘zlar

ReID, Strong Baseline System (SBS), SBS (R50), SBS (R50-IBN), SBS (S50), SBS (R101-IBN) YOLOv11, нейросетевое извлечение признаков, метрическое обучение, видеонаблюдение, re-identification of a person, ReID, Strong Baseline System (SBS), SBS (R50), SBS (R50-IBN), SBS (S50), SBS (R101-IBN), YOLOv11, neural network feature extraction, metric learning, video surveillance

Ilmiy soha

Texnika

Iqtibos keltirish

APAХамдамов, Р.Х., Рахманов, Х.Э., & Раматов, И.И. (2026). Evaluation of person re-identification methods based on four architectures of the SBS family. Рақамли технологияларнинг назарий ва амалий масалалари, 9(1), 49-56. https://doi.org/10.62132/ijdt.v9i1.322
GOST (OAK)Хамдамов, Р.Х., Рахманов, Х.Э., Раматов, И.И. Evaluation of person re-identification methods based on four architectures of the SBS family // Рақамли технологияларнинг назарий ва амалий масалалари. – 2026. – T. 9, № 1. – P. 49-56. – DOI: 10.62132/ijdt.v9i1.322.
MLAХамдамов, Р.Х., Рахманов, Х.Э., and Раматов, И.И. “Evaluation of person re-identification methods based on four architectures of the SBS family.” Рақамли технологияларнинг назарий ва амалий масалалари, 9(1), 49-56, 2026. https://doi.org/10.62132/ijdt.v9i1.322
ChicagoХамдамов, Р.Х., Рахманов, Х.Э., and Раматов, И.И. “Evaluation of person re-identification methods based on four architectures of the SBS family.” Рақамли технологияларнинг назарий ва амалий масалалари, 9(1), 49-56 (2026). https://doi.org/10.62132/ijdt.v9i1.322
HarvardХамдамов, Р.Х., Рахманов, Х.Э., and Раматов, И.И (2026) ‘Evaluation of person re-identification methods based on four architectures of the SBS family’, Рақамли технологияларнинг назарий ва амалий масалалари, 9(1), 49-56. https://doi.org/10.62132/ijdt.v9i1.322
BibTeX
@article{article2026,
  title = {Evaluation of person re-identification methods based on four architectures of the SBS family},
  author = {Хамдамов, Р.Х. and Рахманов, Х.Э. and Раматов, И.И.},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2026},
  volume = {9},
  number = {1},
  pages = {49-56},
  doi = {10.62132/ijdt.v9i1.322},
  url = {https://doi.org/10.62132/ijdt.v9i1.322}
}

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