REAL VAQT REJIMIDAGI VIDEOKUZATUV TIZIMLARIDA OBYEKTLARNI ANIQLASH VA TANISHNING NEYRON TARMOQ USULLARI: SIRKULYAR IQTISODIYOT VA RESURS SAMARADORLIGI YONDASHUVI
Tashkilot
Ichki ishlar vazirligi Tashkiliy departamenti Situatsion-tahliliy markazi smena boshligʻi
Annotatsiya
Maqolada videokuzatuvda obyektlarni aniqlash, kuzatish va yuzni tanishning neyron tarmoq usullari sirkulyar iqtisodiyot nuqtayi nazaridan tizimlashtirildi. Eʼlon qilingan natijalar turli toʻplam va grafik protsessorlarda olingani sababli bevosita taqqoslanmasligi koʻrsatildi. Kadrning toʻliq kechikishini va bitta tezlatgich xizmat koʻrsata oladigan oqimlar sonini baholovchi analitik model taklif etildi: faraziy misolda aniqlashni har beshinchi kadrda chaqirish oqimlar sonini 3 tadan 12 taga oshirdi. Yuzni tanishni cheklash va uskunani tejash tavsiyalari ishlab chiqildi. Tahlil YOLO, Faster R-CNN, YOLOv7, RT-DETR, DeepSORT, ByteTrack, FaceNet va ArcFace modellarining eʼlon qilingan natijalarini qamrab oldi. Kaskad arxitekturasida aniqlash chastotasi, oqimlar sigʻimi va yangi obyektni aniqlash kechikishi oʻrtasidagi kelishuv koʻrsatildi. Tavsiyalar biometrik maʼlumotlarga ishlov berishning huquqiy talablari va operator nazoratiga tayanadi. Taklif etilgan model videoanalitika tizimini loyihalashda talab qilinadigan tezlatgichlar sonini oldindan baholashga imkon beradi.Kalit so'zlar
sunʼiy intellekt, videokuzatuv, obyektlarni aniqlash, neyron tarmoqlar, sirkulyar iqtisodiyot, yuzni tanish, real vaqt rejimi, kechikish
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