A review of deep learning-based multi-object tracking methods

Mirzaaxmedov , Dilmurod

Рақамли иқтисодиёт ва ахборот технологиялари · 2026-yil

Annotatsiya

This paper reviews ten recent influential studies addressing the Multiple Object Tracking (MOT) problem. The analysis focuses on object detection, reidentification, anchor-free methods, deep learning-based approaches, and real-time tracking performance. MOT is a key task in computer vision, aimed at detecting and tracking multiple objects in real-time video streams, with important applications in traffic management, surveillance, healthcare monitoring, and agriculture. Recent advances in deep learning and multi-task learning have significantly improved tracking accuracy and robustness. The reviewed works examine various tracking frameworks, including Siamese-based models and neural network architectures such as CNNs and RNNs. A comparative evaluation is also conducted using a column diagram to assess the effectiveness of different MOT methods, including FairMOT, YOLO, CenterNet, and classical algorithms such as the Kalman filter and Hungarian algorithm

Maqola ma’lumotlari
MualliflarMirzaaxmedov , Dilmurod
JurnalРақамли иқтисодиёт ва ахборот технологиялари
NashriyotТошкент давлат иқтисодиёт университети
Nashr sanasi2026-04-14
Jild6
Son1
Betlar161-167
TilIngliz
Ilmiy sohaIqtisodiyot
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

Multi-Object Tracking, Re-Identification, Deep Learning, Convolutional Neural Networks, Siamese Networks, Real-Time Object Tracking, Object Detection, Trajectory Prediction, Feature Extraction, Online Tracking

Ilmiy soha

Iqtisodiyot

Iqtibos keltirish

APAMirzaaxmedov , Dilmurod (2026). A REVIEW OF DEEP LEARNING-BASED MULTI-OBJECT TRACKING METHODS. Рақамли иқтисодиёт ва ахборот технологиялари, 6(1), 161-167. https://dgeconomy.tsue.uz/index.php/dgeco/article/view/491
GOST (OAK)Mirzaaxmedov , Dilmurod A REVIEW OF DEEP LEARNING-BASED MULTI-OBJECT TRACKING METHODS // Рақамли иқтисодиёт ва ахборот технологиялари. – 2026. – T. 6, № 1. – P. 161-167.
MLAMirzaaxmedov , Dilmurod. “A REVIEW OF DEEP LEARNING-BASED MULTI-OBJECT TRACKING METHODS.” Рақамли иқтисодиёт ва ахборот технологиялари, 6(1), 161-167, 2026. https://dgeconomy.tsue.uz/index.php/dgeco/article/view/491
ChicagoMirzaaxmedov , Dilmurod. “A REVIEW OF DEEP LEARNING-BASED MULTI-OBJECT TRACKING METHODS.” Рақамли иқтисодиёт ва ахборот технологиялари, 6(1), 161-167 (2026). https://dgeconomy.tsue.uz/index.php/dgeco/article/view/491
HarvardMirzaaxmedov , Dilmurod (2026) ‘A REVIEW OF DEEP LEARNING-BASED MULTI-OBJECT TRACKING METHODS’, Рақамли иқтисодиёт ва ахборот технологиялари, 6(1), 161-167. https://dgeconomy.tsue.uz/index.php/dgeco/article/view/491
BibTeX
@article{mirzaaxmedovdilmurod2026,
  title = {A REVIEW OF DEEP LEARNING-BASED MULTI-OBJECT TRACKING METHODS},
  author = {Mirzaaxmedov , Dilmurod},
  journal = {Рақамли иқтисодиёт ва ахборот технологиялари},
  year = {2026},
  volume = {6},
  number = {1},
  pages = {161-167},
  url = {https://dgeconomy.tsue.uz/index.php/dgeco/article/view/491}
}

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