Tel: +998977838464

2026
III International Bioeconomy Forum: Special Issue

IV-SEKSIYA - IQLIMGA MOSLASHUV VA EKOLOGIK MUVOZANATNI SAQLASHGA XIZMAT QILUVCHI INNOVATSION YECHIMLARNI TARGʻIB QILISH

REAL VAQT REJIMIDAGI VIDEOKUZATUV TIZIMLARIDA OBYEKTLARNI ANIQLASH VA TANISHNING NEYRON TARMOQ USULLARI: SIRKULYAR IQTISODIYOT VA RESURS SAMARADORLIGI YONDASHUVI

DOI 10.67668/mj/2026issmaxsus/2193 Pages 281-286

Published 2026-09-25

Authors
Xolmatov Azizbek Tolibjonovich
Affiliation
Ichki ishlar vazirligi Tashkiliy departamenti Situatsion-tahliliy markazi smena boshligʻi
III Bioiqtisodiyot xalqaro forumi, Buxoro, 24-25-sentabr 2026
PDF (Uzbek) Google Scholar Google Scholar

Abstract

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.

Keywords

artificial intelligence, video surveillance, object detection, neural networks, circular economy, face recognition, real-time processing, latency

References

  1. Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). ArcFace: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4685-4694). https://doi.org/10.1109/CVPR.2019.00482
  2. Ellen MacArthur Foundation. (2019). The circular economy in detail. https://www.ellenmacarthurfoundation.org/the-circular-economy-in-detail-deep-dive
  3. European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L, 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
  4. Grother, P., Ngan, M., & Hanaoka, K. (2019). Face recognition vendor test (FRVT) part 3: Demographic effects (NISTIR 8280). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.8280
  5. International Telecommunication Union & United Nations Institute for Training and Research. (2024). Global E-waste Monitor 2024. https://www.itu.int/en/ITU-D/Environment/Pages/Publications/The-Global-E-Waste-Monitor-2024.aspx
  6. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779-788). https://doi.org/10.1109/CVPR.2016.91
  7. Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real- time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
  8. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 815-823). https://doi.org/10.1109/CVPR.2015.7298682
  9. Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7464- 7475). https://doi.org/10.1109/CVPR52729.2023.00721
  10. Wojke, N., Bewley, A., & Paulus, D. (2017). Simple online and realtime tracking with a deep association metric. In 2017 IEEE International Conference on Image Processing (pp. 3645-3649). IEEE. https://doi.org/10.1109/ICIP.2017.8296962
  11. Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., & Wang, X. (2022). ByteTrack: Multi-object tracking by associating every detection box. In Computer Vision - ECCV 2022 (pp. 1-21). Springer. https://doi.org/10.1007/978-3-031-20047-2_1
  12. Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., & Chen, J. (2024). DETRs beat YOLOs on real-time object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16965- 16974). https://doi.org/10.1109/CVPR52733.2024.01605
  13. Oʻzbekiston Respublikasining 2019-yil 2-iyuldagi OʻRQ-547-son “Shaxsga doir maʼlumotlar toʻgʻrisida”gi Qonuni. https://lex.uz/docs/-4396419
  14. Oʻzbekiston Respublikasi Prezidentining 2024-yil 4-yanvardagi PF-5-son “Chiqindilarni boshqarish tizimini takomillashtirish va ularning ekologik vaziyatga salbiy taʼsirini kamaytirish boʻyicha chora-tadbirlar toʻgʻrisida”gi Farmoni. https://lex.uz/docs/-6732832
  15. Spot.uz. (2025, 14-iyul). Toshkentda SIʼga ega kameralar piyodalarga yoʻl bermayotgan haydovchilarni aniqlay boshladi. https://www.spot.uz/oz/2025/07/14/cameras-ai/

Downloads

Download data is not yet available.