KUZATUV KAMERALARI VIDEOMA’LUMOTLARI ASOSIDA TALABALAR DAVOMATINI HISOBGA OLISHNING INTELLEKTUAL AXBOROT TIZIMI ALGORITMLARI VA DASTURIY TA’MINOTI
DOI:
https://doi.org/10.5281/zenodo.23358711Abstract
Oliy ta’lim muassasalarida talabalar davomatini an’anaviy hisobga olish usullari ko‘p vaqt talab etadi va xatoliklarga moyil. Ushbu maqolada kuzatuv kameralari videoma’lumotlari asosida ishlovchi avtomatlashtirilgan intellektual axborot tizimi arxitekturasi va algoritmlari taklif etilgan. Tizim yuzni tezkor aniqlash uchun YOLOv8 va shaxsni biometrik identifikatsiya qilish uchun FaceNet modellariga asoslangan. Tadqiqot natijalari tizimning real vaqt rejimida 96,8% aniqlikda ishlashini isbotladi. Shuningdek, ma’lumotlarni qayta ishlash serveriga tushadigan yuklamani optimallashtirish bo‘yicha amaliy takliflar ishlab chiqilgan.Keywords
axborot tizimlari, kompyuter ko‘rishi, yuzni tanib olish, YOLOv8, FaceNet, avtomatlashtirilgan davomat, videoma’lumotlarni qayta ishlashReferences
Chintalapati, S., & Raghunadh, M. V. (2013). “Automated attendance management system based on face recognition algorithms”. 2013 IEEE International Conference on Computational Intelligence and Computing Research, 1-5.
Arsenovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. (2017). “FaceTime - Deep learning based face recognition attendance system”. 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), 000053-000056.
Rekha, E., & Ramaprasad, P. (2017). “An efficient automated attendance management system based on face recognition”. 2017 5th International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 60-63.
Weng, C. H., Lai, Y. H., & Lai, Z. C. (2020). “A real-time face recognition system for student attendance”. 2020 International Conference on System Science and Engineering (ICSSE), 1-4.
Kar, N., Debbarma, M. K., Saha, A., & Pal, D. R. (2012). “Study of implementing automated attendance system using face recognition technique”. International Journal of Computer and Communication Engineering, 1(2), 100.
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). “FaceNet: A unified embedding for face recognition and clustering”. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 815-823.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). “You only look once: Unified, real-time object detection”. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 779-788.
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). “ArcFace: Additive angular margin loss for deep face recognition”. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 4690-4699. 9. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). “DeepFace: Closing the gap to human-level performance in face verification”. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 1701-1708.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). “Deep residual learning for image recognition”. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 770-778.
Terven, J., & Cordova-Esparza, D. (2023). “A comprehensive review of YOLO: From YOLOv1 and beyond”. arXiv preprint arXiv:2304.00501.
Viola, P., & Jones, M. (2001). “Rapid object detection using a boosted cascade of simple features”. Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition (CVPR), Vol. 1, I-511. 13. Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). “Face recognition: A literature survey”. ACM computing surveys (CSUR), 35(4), 399-458.
Lugaresi, C., et al. (2019). “MediaPipe: A framework for building perception pipelines”. arXiv preprint arXiv:1906.08172.
King, D. E. (2009). “Dlib-ml: A machine learning toolkit”. Journal of Machine Learning Research, 10, 1755-1758.
Askaraliyev, O. U., Malikova, N. T., Naim, N. A., Ungbaeva, D. U., & Islamova, F. K. (2022). Modeling the process of intellectual support for optimal decision-making in management systems. NeuroQuantology | May, 2144–2158. https://doi.org/10.14704/nq.2022.20.5.NQ22575
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