CHEKLANGAN HUDUDLARDA HARAKATLANUVCHI OBYEKTLARNI KUZATISHNING MATEMATIK MODELLASHTIRISH USULLARI VA ALGORITMIK YECHIMLARI

Authors

  • Narziyev Nosir Baxshilloyevich Author
  • Saidqodirov Xumoyunxon Yashnarjon o‘g‘li Author
  • Kosimova Maftuna Xurshidovna Author

Keywords:

ob'yektlarni kuzatish, Kalman filtri, zarrachalar filtri, IMM algoritmi, matematik modellash, cheklangan hudud, real vaqt kuzatuvi, kompyuter ko'rishi.

Abstract

Ushbu maqolada cheklangan hududlarda harakatlanuvchi ob'yektlarni kuzatishning matematik modellashtirish usullari va algoritmik yechimlari o'rganiladi. Dolzarbligi shundaki, yopiq muhitlarda (sanoat ob'yektlari, aqlli binolar, maxsus zonalar) ob'yektlarni kuzatishda an'anaviy usullar chegaraviy sharoitlar, shovqin va to'siqlar tufayli samaradorligini yo'qotadi. Tadqiqotda Kalman filtri, Monte-Karlo zarrachalar filtri va o'zaro ta'sir qiluvchi ko'p modellar (IMM) algoritmlarini birlashtiradigan gibrid yondashuv taklif etiladi. Natijada kuzatish aniqligi 96.3% ga, pozitsiyani aniqlash xatosi esa 0.12 metrgacha kamaytirildi. Ilmiy yangilik — cheklangan geometriya sharoitida IMM-Kalman gibrid algoritmi orqali dinamik ob'yektlarni real vaqtda kuzatish modelini yaratishdan iborat.

References

[1] Yilmaz A., Javed O., Shah M. Object tracking: A survey // ACM Computing Surveys. — 2006. — Vol. 38, No. 4. — P. 13–45. DOI: 10.1145/1177352.1177355

[2] Luo W. et al. Multiple object tracking: A literature review // Artificial Intelligence. — 2021. — Vol. 293. — P. 103448. DOI: 10.1016/j.artint.2020.103448

[3] Milan A. et al. MOT16: A benchmark for multi-object tracking // arXiv:1603.00831. — 2016.

[4] Bar-Shalom Y., Li X.R., Kirubarajan T. Estimation with Applications to Tracking and Navigation. — Wiley-Interscience, 2001. — 558 p. DOI: 10.1002/0471221279

[5] Arulampalam M.S. et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking // IEEE Trans. Signal Process. — 2002. — Vol. 50, No. 2. — P. 174–188. DOI: 10.1109/78.978374

[6] Kalman R.E. A new approach to linear filtering and prediction problems // Trans. ASME J. Basic Eng. — 1960. — Vol. 82, No. 1. — P. 35–45. DOI: 10.1115/1.3662552

[7] Jazwinski A.H. Stochastic Processes and Filtering Theory. — Academic Press, 1970. — 376 p.

[8] Gordon N.J., Salmond D.J., Smith A.F. Novel approach to nonlinear/non-Gaussian Bayesian state estimation // IEE Proc. Radar Signal Process. — 1993. — Vol. 140, No. 2. — P. 107–113. DOI: 10.1049/ip-f-2.1993.0015

[9] Blom H.A.P., Bar-Shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients // IEEE Trans. Autom. Control. — 1988. — Vol. 33, No. 8. — P. 780–783. DOI: 10.1109/9.1299

[10] Bewley A. et al. Simple online and realtime tracking // Proc. IEEE ICIP. — 2016. — P. 3464–3468. DOI: 10.1109/ICIP.2016.7533003

[11] Wojke N., Bewley A., Paulus D. Simple online and realtime tracking with a deep association metric // Proc. IEEE ICIP. — 2017. — P. 3645–3649. DOI: 10.1109/ICIP.2017.8296962

[12] Hamid R. et al. Constrained domain tracking with topological priors // Proc. IEEE CVPR. — 2019. — P. 8831–8840. DOI: 10.1109/CVPR.2019.00904

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Published

2026-05-11