VIDEO TASVIRLARDA HARAKATDAGI OBYEKTLARNI ANIQLASHNING INTELLEKTUAL TIZIMINI ISHLAB CHIQISH

Authors

  • Obilov Bahodir Toshkent Kimyo xalqaro universiteti Namangan filiali, magistranti.

Keywords:

Kompyuter ko‘rish, obyekt aniqlash, ko‘p obyektli kuzatuv, YOLO, DETR, SORT, ByteTrack, FairMOT, MOTChallenge, real vaqt.

Abstract

Ushbu maqolada video oqimlarda harakatdagi obyektlarni aniqlash va kuzatish uchun zamonaviy intellektual tizim arxitekturasi taklif etiladi. Maqola YOLO, Faster/Mask R‑CNN, SSD, DETR kabi detektorlar hamda SORT/DeepSORT, FairMOT, ByteTrack singari ko‘p obyektli kuzatuv (MOT) usullari tahliliga tayangan holda, real vaqt talablari (FPS), aniqlik ko‘rsatkichlari (mAP, IDF1) va resurs cheklovlari o‘rtasidagi muvozanatni yoritadi. Shuningdek, O‘zbekistonda yo‘l harakatini monitoring qilish bo‘yicha nashrlardan amaliy misollar keltiriladi.

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References

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[13] SORT GitHub. https://github.com/abewley/sort

[14] Google Research copy of SSD paper. https://research.google.com/pubs/archive/44872.pdf

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[19] ResearchGate copy: Deep SORT. https://www.researchgate.net/publication/315491564

[20] Springer chapter: DETR. https://link.springer.com/chapter/10.1007/978-3-030-58452-8_13

[21] CVPR 2023 version of YOLOv7. https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_YOLOv7_Trainable_Bag-of-Freebies_Sets_New_State-of-the-Art_for_Real-Time_Object_Detectors_CVPR_2023_paper.pdf

[22] Ultralytics Docs (home). https://docs.ultralytics.com/

[23] MOT20 benchmark. https://arxiv.org/abs/2003.09003

[24] Ultralytics: YOLOv8 vs YOLOv5. https://docs.ultralytics.com/compare/yolov8-vs-yolov5/

[25] MOT17 dataset page. https://motchallenge.net/data/MOT17/

[26] MOTChallenge FAQ. https://motchallenge.net/faq/

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Published

2026-04-01

How to Cite

VIDEO TASVIRLARDA HARAKATDAGI OBYEKTLARNI ANIQLASHNING INTELLEKTUAL TIZIMINI ISHLAB CHIQISH. (2026). INTERNATIONAL CONFERENCE ON INTERDISCIPLINARY SCIENCE, 3(3), 297-301. https://universalconference.us/index.php/icms/article/view/6946