Real vaqt rejimidagi videokuzatuv tizimlarida obyektlarni aniqlash va tanishning neyron tarmoq usullari: sirkulyar iqtisodiyot va resurs samaradorligi yondashuvi

Xolmatov Azizbek Tolibjonovich

Marketing · 2026-yil

Annotatsiya

The article systematized neural network methods for object detection, tracking and face recognition in video surveillance from a circular economy perspective. It showed that published results were not directly comparable because of different datasets and GPUs. An analytical model of end-to-end frame latency and of the number of streams per accelerator was proposed: in a hypothetical example, invoking the detector on every fifth frame raised the number of streams from 3 to 12. Recommendations to restrict face recognition and save hardware were developed. The analysis covered the published results of YOLO, Faster R-CNN, YOLOv7, RT-DETR, DeepSORT, ByteTrack, FaceNet and ArcFace. The trade-off between detection frequency, stream capacity and the delay in detecting a new object in a cascade architecture was shown. The recommendations rely on the legal requirements for processing biometric data and on operator oversight. The proposed model makes it possible to estimate in advance the number of accelerators required when designing a video analytics system.

Maqola ma’lumotlari
MualliflarXolmatov Azizbek Tolibjonovich
JurnalMarketing
NashriyotТошкент давлат иқтисодиёт университети
ISSN3060-4621
Nashr sanasi2026-09-25
Betlar281-286
TilO‘zbek
Ilmiy sohaIqtisodiyot
DOI10.67668/mj/2026issmaxsus/2193
Indeksga qo‘shilgan2026-10-18

DOI: 10.67668/mj/2026issmaxsus/2193 · Maqolaning asl sahifasi

Kalit so‘zlar

sunʼiy intellekt, videokuzatuv, obyektlarni aniqlash, neyron tarmoqlar, sirkulyar iqtisodiyot, yuzni tanish, real vaqt rejimi, kechikish, искусственный интеллект, видеонаблюдение, обнаружение объектов, нейронные сети, циркулярная экономика, распознавание лиц, режим реального времени, задержка, artificial intelligence, video surveillance, object detection, neural networks, circular economy, face recognition, real-time processing, latency

Ilmiy soha

Iqtisodiyot

Iqtibos keltirish

APAXolmatov Azizbek Tolibjonovich (2026). REAL VAQT REJIMIDAGI VIDEOKUZATUV TIZIMLARIDA OBYEKTLARNI ANIQLASH VA TANISHNING NEYRON TARMOQ USULLARI: SIRKULYAR IQTISODIYOT VA RESURS SAMARADORLIGI YONDASHUVI. Marketing, 281-286. https://doi.org/10.67668/mj/2026issmaxsus/2193
GOST (OAK)Xolmatov Azizbek Tolibjonovich REAL VAQT REJIMIDAGI VIDEOKUZATUV TIZIMLARIDA OBYEKTLARNI ANIQLASH VA TANISHNING NEYRON TARMOQ USULLARI: SIRKULYAR IQTISODIYOT VA RESURS SAMARADORLIGI YONDASHUVI // Marketing. – 2026. – B. 281-286. – DOI: 10.67668/mj/2026issmaxsus/2193.
MLAXolmatov Azizbek Tolibjonovich. “REAL VAQT REJIMIDAGI VIDEOKUZATUV TIZIMLARIDA OBYEKTLARNI ANIQLASH VA TANISHNING NEYRON TARMOQ USULLARI: SIRKULYAR IQTISODIYOT VA RESURS SAMARADORLIGI YONDASHUVI.” Marketing, 281-286, 2026. https://doi.org/10.67668/mj/2026issmaxsus/2193
ChicagoXolmatov Azizbek Tolibjonovich. “REAL VAQT REJIMIDAGI VIDEOKUZATUV TIZIMLARIDA OBYEKTLARNI ANIQLASH VA TANISHNING NEYRON TARMOQ USULLARI: SIRKULYAR IQTISODIYOT VA RESURS SAMARADORLIGI YONDASHUVI.” Marketing, 281-286 (2026). https://doi.org/10.67668/mj/2026issmaxsus/2193
HarvardXolmatov Azizbek Tolibjonovich (2026) ‘REAL VAQT REJIMIDAGI VIDEOKUZATUV TIZIMLARIDA OBYEKTLARNI ANIQLASH VA TANISHNING NEYRON TARMOQ USULLARI: SIRKULYAR IQTISODIYOT VA RESURS SAMARADORLIGI YONDASHUVI’, Marketing, 281-286. https://doi.org/10.67668/mj/2026issmaxsus/2193
BibTeX
@article{xolmatovazizbektolibjonovich2026,
  title = {REAL VAQT REJIMIDAGI VIDEOKUZATUV TIZIMLARIDA OBYEKTLARNI ANIQLASH VA TANISHNING NEYRON TARMOQ USULLARI: SIRKULYAR IQTISODIYOT VA RESURS SAMARADORLIGI YONDASHUVI},
  author = {Xolmatov Azizbek Tolibjonovich},
  journal = {Marketing},
  year = {2026},
  pages = {281-286},
  doi = {10.67668/mj/2026issmaxsus/2193},
  url = {https://doi.org/10.67668/mj/2026issmaxsus/2193}
}

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