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Bugungi kunda shaxsni tanib olish va identifikatsiya qilishning bir nechta usullari mavjud bo‘lib, ular kundan-kunga takomillashib bormoqda. Biroq bu usullarni qalbakilashtirish holatlari ham kuzatilmoqda. Zamonaviy videokuzatuv tizimlari rivojlanib, shu tizimlar yordamida ma’lum bir hududda sodir bo‘layotgan barcha voqea-hodisalarni suratga olish hamda olingan ma’lumotlarni neyron tarmoqlar yordamida tezkor va samarali tahlil qilish imkoniyatlari paydo bo‘lmoqda. Videotasvirlar orqali shaxs harakatini kuzatish, taqiqlangan hududga noqonuniy kirishni aniqlash, kameralardan olingan surat yordamida jinoyatchilarni topish, qidiruvdagi jinoyatchilarning boshqa biometrik ma’lumotlarni o‘zlashtirgan holda, xorijiy davlatlarga chiqish yoki kirishini nazorat qilish mumkin. Ushbu tizim aeroportlar, temir yo‘l vokzallari, dengiz portlarida jinoyatchilarni ushlashga yordam beradi, bir qatorda yoki olomonda bo‘lgan odamlar sonini avtomatik ravishda sanaydi va ularning harakatlari xarakterini tahlil qiladi. Bu esa insonning subyektiv aralashuvi va ma’lumotlarni qayta ishlash uchun zarur bo‘lgan vaqtni kamaytiradi. Bundan tashqari, sportchilar harakatlarida ham pozani neyron tarmoqlar yordamida baholash hozirda keng qo‘llanilmoqda. Xususan, sportchilarni guruhdan ajratib olish va shaxsiyatga qarab xatti-harakatlarini o‘rganish, buning natijasida sport mashg‘ulotlari samaradorligini oshirish mumkin

  • O'qishlar soni11
  • Nashr sanasi22-06-2023
  • Asosiy tilO'zbek
  • Sahifalar33-43
Ўзбек

Bugungi kunda shaxsni tanib olish va identifikatsiya qilishning bir nechta usullari mavjud bo‘lib, ular kundan-kunga takomillashib bormoqda. Biroq bu usullarni qalbakilashtirish holatlari ham kuzatilmoqda. Zamonaviy videokuzatuv tizimlari rivojlanib, shu tizimlar yordamida ma’lum bir hududda sodir bo‘layotgan barcha voqea-hodisalarni suratga olish hamda olingan ma’lumotlarni neyron tarmoqlar yordamida tezkor va samarali tahlil qilish imkoniyatlari paydo bo‘lmoqda. Videotasvirlar orqali shaxs harakatini kuzatish, taqiqlangan hududga noqonuniy kirishni aniqlash, kameralardan olingan surat yordamida jinoyatchilarni topish, qidiruvdagi jinoyatchilarning boshqa biometrik ma’lumotlarni o‘zlashtirgan holda, xorijiy davlatlarga chiqish yoki kirishini nazorat qilish mumkin. Ushbu tizim aeroportlar, temir yo‘l vokzallari, dengiz portlarida jinoyatchilarni ushlashga yordam beradi, bir qatorda yoki olomonda bo‘lgan odamlar sonini avtomatik ravishda sanaydi va ularning harakatlari xarakterini tahlil qiladi. Bu esa insonning subyektiv aralashuvi va ma’lumotlarni qayta ishlash uchun zarur bo‘lgan vaqtni kamaytiradi. Bundan tashqari, sportchilar harakatlarida ham pozani neyron tarmoqlar yordamida baholash hozirda keng qo‘llanilmoqda. Xususan, sportchilarni guruhdan ajratib olish va shaxsiyatga qarab xatti-harakatlarini o‘rganish, buning natijasida sport mashg‘ulotlari samaradorligini oshirish mumkin

Русский

Сегодня существует несколько способов узнать и идентифицировать человека, и они совершенствуются день ото дня. Однако наблюдаются и случаи фальсификации этих методов. Современные системы видеонаблюдения развиваются, с помощью спутниковых систем можно фотографировать все, что происходит в определенной местности, а затем быстро и эффективно проанализировать полученные данные на основе нейронных сетей. Благодаря видеоизображениям и фотографиям с камер наблюдения можно следить за перемещением любого человека, выявлять факты незаконного проникновения в запрещенную зону, распознавать разыскиваемых преступников, не допускать их выезд или проникновение в зарубежные страны, получать другие биометрические данные. Эта система помогает выявлять и задерживать преступников в аэропортах, вокзалах, морских портах, автоматически подсчитывать количество людей в очереди или толпе, анализировать характер их перемещений, это снижает степень субъективного вмешательства человека и сокращает время обработки данных. Кроме того, анализ на основе нейронных сетей положения тела теперь широко используется при оценке движений спортсменов, в частности для выделения отдельного спортсмена из группы, определения и изучения его двигательных способностей и повышения эффективности упражнений в зависимости от выявленных особенностей.

English

Nowadays, there are several ways of recognizing and identifying a person, and these keep enhancing day by day. However, cases of falsification of these methods can also be observed. Modern video surveillance systems keep on developing, and satellite systems enable us to photograph everything that happens in a certain area and analyze effectively the data obtained using neural networks. Video images help to monitor motions of people, detect illegal entries into prohibited areas, as well as to identify remaining criminals by means of photos taken from cameras and control exit and/or entry of wanted criminals to foreign countries by acquiring other biometric data. This system serves to catch criminals at airports, railway stations, seaports; it automatically counts numbers of people in a line or in a crowd and analyzes the nature of their movements, which lessens the amount of subjective human intervention and reduces time required for data processing. Moreover, neural network-based pose estimation system is now being used widely in sports, and this can distinguish between athletes and change their behavior based on their personal characteristics.

Muallifning F.I.Sh. Lavozimi Tashkilot nomi
1 Ximmatov I.Q. “Matematik modellashtirish” kafedrasi tayanch doktoranti Sharof Rashidov nomidagi Samarqand davlat universiteti
Havola nomi
1 Аkhаtov, A., Nazarov, F., & Rashidov, A. (2021). Increasing data reliability by using bigdata parallelization mechanisms. Proceedings of the International Conference on Information Science and Communications Technologies , (pp. 1-4). Tashkent. doi:10.1109/ICISCT52966.2021.9670387
2 Akhatov, A., & Djumanov, O. (2006). Mechanisms of images visualization in the system of microobjects recognition and classification. Proceedings of the Fourth World Conference on Intelligent Systems for Industrial Automation, (pp. 206-210). Tashkent.
3 Akhatov, A., Nazarov, F., & Rashidov, A. (2022). Mechanisms of Information Reliability in BigData and Blockchain Technologies. Proceedings of the International Conference on Information Science and Communications Technologies, (pp. 1-4). Tashkent. doi:10.1109/ICISCT52966.2021.9670052
4 Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014). 2D Human Pose Estimation: New Benchmark and State of the Art Analysis. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 3686-3693). Columbus, OH, USA: IEEE . doi:10.1109/CVPR.2014.471
5 Axatov, A., & Ximmatov, I. (2020). Foydalanuvchilarning biometrik autentifikatsiya turlari asosida haqiqiyligini tasdiqlash usullarining samaradorligi [Effectiveness of user authentication methods based on biometric authentication types]. Proceedings of the Conference Innovative approaches as a key to scientific progress: solutions and perspectives, (pp. 20-26). Jizzakh, Uzbekistan.
6 Bourdev, L., & Malik, J. (2009). Poselets: Body part detectors trained using 3D human pose annotations. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision (pp. 1365- 1372). Kyoto, Japan: IEEE. doi:10.1109/ICCV.2009.5459303
7 Bulat, A., & Tzimiropoulos, G. (2016). Human pose estimation via convolutional part heatmap regression. Proceedings of the Computer Vision–ECCV 2016: 14th European Conference. VII (14), pp. 717-732. Amsterdam, Netherlands: Springer International Publishing. doi:10.1007/978-3-319- 46478-7_44
8 Chang, M., Qi, H., Wang, X., Cheng, H., & Lyu, S. (2015). Fast Online Upper Body Pose Estimation from Video. В X. Xie, M. Jones, & G. Tam (Ред.), Proceedings of the British Machine Vision Conference, (pp. 104.1-104.12). doi:10.5244/C.29.104
9 Cherian, A., Mairal, J., Alahari, K., & Schmid, C. (2014). Anoop & MaiMixing Body-Part Sequences for Human Pose Estimation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2361–2368). IEEE. doi:10.1109/CVPR.2014.302
10 Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A., & Wang, X. (2017). Multi-context attention for humanpose estimation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (pp. 5669-5678). Honolulu, USA: IEEE. doi:10.48550/arXiv.1702.07432
11 Liu, Z., Zhu, J., Bu, J., & Chen, C. (2015). A survey of human pose estimation. Vis. Comun. Image Represent, 32(C), 10-19. doi:10.1016/j.jvcir.2015.06.013
12 Newell, A., Yang, K., & Deng, J. (2016). Stacked Hourglass Networks for Human Pose Estimation. Proceedings of the Computer Vision–ECCV 2016: 14th European Conference. VIII (14), pp. 483-499. Amsterdam, Netherlands: Springer International Publ. doi:10.48550/arXiv.1603.06937
13 Sapp, B., & Taskar, B. (2013). Multimodal decomposable models for human pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 3674- 3681). Retrieved from https://openaccess.thecvf.com/content_cvpr_2013/papers/Sapp_MODEC_ Multimodal_Decomposable_2013_CVPR_paper.pdf
14 Ximmatov, I. (2020). Advantages of biometrik gait recognition. Important factors in evaluation of gait analysis systems. Scientific journal of SamSU, 3(121), 104-107.
15 Ximmatov, I. (2020). Important factors in evaluation of gait analysis systems and ad-vantages of biometric gait recognition. Proceedings of the Conference of the Prospects for the use of innovative and modern information technologies in the fields of education, science and management, (pp. 262-267). Samarkand, Uzbekistan.
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