YONGʻINNI VIDEOTASVIRDA RANGLI  FILTRLASH BILAN INTENSIVLIK OʻZGARISHI  ASOSIDA ANIQLASH

Axatov  Akmal Rustamovich; Tojiyev Maruf Ruzikulovich; Shirinboyev Ravshan Shirinboy o'g'li

doi:https://dx.doi.org/10.36522/2181-9637-2023-2-2

279

Videoanalitika va kompyuter koʻrishi videoma’lumotlari yongʻinni avtomatik aniqlash imkonini beradi. Mazkur maqolada videotasvirdan yongʻinni aniqlashning samarali usullarini topish uchun turli xil algoritmlar amalga oshirildi. Shunday algoritmlardan biri sifatida rangga asoslangan yongʻinni aniqlash algoritmi tasvirlangan. Ushbu yondashuv asosida yongʻinni aniqlashda rang modelining oʻzidan foydalanish samarali natija bera olmaydi. Videotasvirdan yongʻinga oʻxshash obyektlardan yongʻinni ajratib olishda piksellar intensivligining vaqtinchalik oʻzgarishini baholash usulidan foydalanilgan. Bunda kadrlar ketma-ketligida intensivlikning oʻrtacha qiymati olinadi. Taklif etilayotgan usul samaradorligini koʻrsatish uchun OpenCV (Open Source Computer Vision Library) kutubxonasidan foydalanib, Python dasturlash tilida dasturiy ta’minot ishlab chiqildi va natijalar olindi.

Name of journalИЛМ-ФАН ВА ИННОВАЦИОН РИВОЖЛАНИШ
Number of editionИлм-фан ва инновацион ривожланиш илмий журнали 2023 йил 2-сон
View count 279

Web Address https://indep-ilm.uz/index.php/journal/article/view/397

DOIhttps://dx.doi.org/10.36522/2181-9637-2023-2-2

Date of creation in the UzSCI system 27-07-2023

Read count 136

Date of publication 14-04-2023

Main LanguageO'zbek

Pages13-21

Tags

videotasvir

RGB model

HSV model

kompyuter koʻrishi

intensivlik

yongʻin pikseli

Ўзбек

Videoanalitika va kompyuter koʻrishi videoma’lumotlari yongʻinni avtomatik aniqlash imkonini beradi. Mazkur maqolada videotasvirdan yongʻinni aniqlashning samarali usullarini topish uchun turli xil algoritmlar amalga oshirildi. Shunday algoritmlardan biri sifatida rangga asoslangan yongʻinni aniqlash algoritmi tasvirlangan. Ushbu yondashuv asosida yongʻinni aniqlashda rang modelining oʻzidan foydalanish samarali natija bera olmaydi. Videotasvirdan yongʻinga oʻxshash obyektlardan yongʻinni ajratib olishda piksellar intensivligining vaqtinchalik oʻzgarishini baholash usulidan foydalanilgan. Bunda kadrlar ketma-ketligida intensivlikning oʻrtacha qiymati olinadi. Taklif etilayotgan usul samaradorligini koʻrsatish uchun OpenCV (Open Source Computer Vision Library) kutubxonasidan foydalanib, Python dasturlash tilida dasturiy ta’minot ishlab chiqildi va natijalar olindi.

Tags

videotasvir

RGB model

HSV model

kompyuter koʻrishi

intensivlik

yongʻin pikseli

Русский

В работе рассматриваются достижения в области видеоаналитики и компьютерного зрения, позволяющие автоматически обнаруживать возгорания по видеоданным. Для поиска эффективных методов обнаружения пожара по видео реализованы различные алгоритмы. В качестве одного из них описывается алгоритм обнаружения пожара на основе цвета. Однако использование только одной цветовой модели для обнаружения пожара является неэффективным подходом. В работе предложен метод оценки временных изменений интенсивности пикселей, который используется для извлечения информации о возгораниях из огнеподобных объектов на видеоизображении. В данном методе вычисляется среднее значение интенсивности в последовательности кадров. Для демонстрации эффективности предложенного метода было разработано программное обеспечение на языке программирования Python с использованием библиотеки OpenCV (Open Source Computer Vision Library), были получены соответствующие результаты.

Tags

интенсивность

видеоизображение

RGB-модель

HSV-модель

компьютерное зрение

пиксель огня

English

The paper reviews achievements made in the field of video analytics and computer vision, which enable the automatic detection of fires, based on video data. Various algorithms have been implemented in sought for effective fire detection methods using video. As one of these, a colour-based fire detection algorithm is being described. However, using only one colour model for fire detection is an inefficient approach. The paper proposes a method for estimating temporal changes in pixel intensity, which is used to retrieve information about fires from fire-like objects in a video image. This method helps to calculate the average intensity value in a sequence of shots. A special software has been developed in the Python programming language using the OpenCV (Open Source Computer Vision Library) library, and the corresponding findings have been gained in view to demonstrate the effectiveness of the proposed method.

Tags

intensity

computer vision

RGB model

HSV model

video image

fire pixel

№ Author name position Name of organisation

1 Axatov A.R. texnika fanlari doktori, professor, xalqaro hamkorlik bo‘yicha prorektor Sharof Rashidov nomidagi Samarqand davlat universiteti

2 Tojiyev M.R. “Kompyuter ilmlari va dasturlashtirish” kafedrasi doktoranti Mirzo Ulugʻbek nomidagi O‘zbekiston Milliy universiteti Jizzax filial

3 Shirinboyev R.S. “Kompyuter ilmlari va dasturlashtirish” kafedrasi magistranti Mirzo Ulugʻbek nomidagi O‘zbekiston Milliy universiteti Jizzax filial

№ Name of reference

1 Celik, T., Demirel, H., Ozkaramanli, H., & Uyguroglu, M. (2006). Fire detection using statistical colour model in video sequences. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal, pp. 213-216.

2 Celik, T., Ozkaramanli, H., & Demirel, H. (2007). Fire and smoke detection without sensors: image processing based approach. Proceedings of the European Signal Processing Conference.

3 Chen, T., Wu, P., & Chiou, Y. (2004). An early fire-detection method based on image processing. Proceedings of the IEEE International Conf. on Image Processing (ICIP), pp. 1707-1710.

4 Drago, F., Myszkowski, K., Annen, T., & Chiba, N. (2003). Adaptive logarithmic mapping for displaying high contrast scenes. Comput. Graph. Forum(22), pp. 419-426.

5 Duan, J., Bressan, M., Dance, C., & Qui, G. (2010). Tone-mapping high dynamic range images by novel histogram adjustment. Pattern Recognit(43), pp. 1847–1862.

6 Durand, F., & Dorsey, J. (2000). Interactive Tone Mapping, in: Rendering Techniques. Proceedings of the Eurographics Workshop, (pp. 219-230). Brno, Czech Republic.

7 Durand, F., & Dorsey, J. (2002). Fast Bilateral Filtering for the Display of High-Dynamic-Range Images. ACM Trans. Gr.(21), pp. 257-299.

8 Ferradans, S., Bertalmio, M., Provenzi, E., & Caselles, V. (2011). An analysis of visual adaptation and contrast perception for tone mapping. Pattern Anal. Mach. Intell.(33), pp. 2002-2012.

9 Ferwerda, J., Pattanaik, S., Shirley, P., & Green-Berg, D. (1996). A model of visual adaptation for realistic image synthesis. Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, (p. 98). New York.

10 Khasanov, D., Tojiyev, M., & Primqulov, O. (n.d.). Gradient Descent In Machine. Proceedings oif the International Conference on Information Science and Communications Technologies (ICISCT). Retrieved from https://ieeexplore.ieee.org/document/9670169

11 Larson, G., Rushmeier, H., & Piatko, C. (1997). A visibility matching tone reproduction operator for high dynamic range scenes. IEEE Trans. Vis. Comput. Graph.(3), pp. 291-306.

12 Philips, W., Shah, M., & Lobo, N. (2007). Flame recognition in video. Istanbul.

13 Reinhard, E., Stark, M., & Shirley, P. (2002). Photographic tone reproduction for digital images. ACM Trans. Graph.(21), pp. 267-276.

14 Ruzikulovich, T. (2022). Neyron tarmoq algoritmlari yordamida murakkab fondagi belgilarni aniqlash algoritmlari [Algorithms for detecting characters in complex backgrounds using neural network algorithms]. International Journal of Contemporary Scientific and Technical Research, pp. 238-241.

15 Tojiyev, M., Shirinboyev, R., & Sulaymonova, M. (2022). OpenCV kutubxonasida tasvirlarga rang modellari bilan ishlov berish [Processing images with color models in the OpenCV library]. Current Problems and Development Trends of modern Innovation Research: Solutions and Perspectives, 1(1), pp. 212-215.

16 Tojiyev, M., Ulug‘murodov, S., & Shirinboyev, R. (2022). Tasvirlar sifatini yaxshilashning chiziqli kontrast usuli [A linear contrast method for improving the quality of images]. Current Problems and Development Trends of Modern Innovation Research: Solutions and Perspectives, 1(1), pp. 215-217.

17 Toreyin, B., & Cetin, A. (2007). Online detection of fire in video. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-5. Minneapolis.

18 Tozhiyev, M., Primqulov, O., & Khasanov, D. (n.d.). Image segmentation in OpenCV and Python. doi:10.5958/2249-7137.2020.01735.8

19 Tumblin, J., & Rushmeier, H. (1993). Tone reproduction for realistic images. IEEE Comput. Graph. Appl.(13), pp. 42-48.

20 Xolboyevich, A. (2022). Pythonda chiziqli regressiya [Linear Regression in Python]. International Journal of Contemporary Scientific and Technical Research, pp. 233-238.

21 Zhang, J., Zhuang, J., & Du, H. (2006). A new flame detection method using probability model. Proceedings of the International Conference on Computational Intelligence and Security, pp. 1614–1617.

Waiting

№	Author name	position	Name of organisation
1	Axatov A.R.	texnika fanlari doktori, professor, xalqaro hamkorlik bo‘yicha prorektor	Sharof Rashidov nomidagi Samarqand davlat universiteti
2	Tojiyev M.R.	“Kompyuter ilmlari va dasturlashtirish” kafedrasi doktoranti	Mirzo Ulugʻbek nomidagi O‘zbekiston Milliy universiteti Jizzax filial
3	Shirinboyev R.S.	“Kompyuter ilmlari va dasturlashtirish” kafedrasi magistranti	Mirzo Ulugʻbek nomidagi O‘zbekiston Milliy universiteti Jizzax filial

№	Name of reference
1	Celik, T., Demirel, H., Ozkaramanli, H., & Uyguroglu, M. (2006). Fire detection using statistical colour model in video sequences. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal, pp. 213-216.
2	Celik, T., Ozkaramanli, H., & Demirel, H. (2007). Fire and smoke detection without sensors: image processing based approach. Proceedings of the European Signal Processing Conference.
3	Chen, T., Wu, P., & Chiou, Y. (2004). An early fire-detection method based on image processing. Proceedings of the IEEE International Conf. on Image Processing (ICIP), pp. 1707-1710.
4	Drago, F., Myszkowski, K., Annen, T., & Chiba, N. (2003). Adaptive logarithmic mapping for displaying high contrast scenes. Comput. Graph. Forum(22), pp. 419-426.
5	Duan, J., Bressan, M., Dance, C., & Qui, G. (2010). Tone-mapping high dynamic range images by novel histogram adjustment. Pattern Recognit(43), pp. 1847–1862.
6	Durand, F., & Dorsey, J. (2000). Interactive Tone Mapping, in: Rendering Techniques. Proceedings of the Eurographics Workshop, (pp. 219-230). Brno, Czech Republic.
7	Durand, F., & Dorsey, J. (2002). Fast Bilateral Filtering for the Display of High-Dynamic-Range Images. ACM Trans. Gr.(21), pp. 257-299.
8	Ferradans, S., Bertalmio, M., Provenzi, E., & Caselles, V. (2011). An analysis of visual adaptation and contrast perception for tone mapping. Pattern Anal. Mach. Intell.(33), pp. 2002-2012.
9	Ferwerda, J., Pattanaik, S., Shirley, P., & Green-Berg, D. (1996). A model of visual adaptation for realistic image synthesis. Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, (p. 98). New York.
10	Khasanov, D., Tojiyev, M., & Primqulov, O. (n.d.). Gradient Descent In Machine. Proceedings oif the International Conference on Information Science and Communications Technologies (ICISCT). Retrieved from https://ieeexplore.ieee.org/document/9670169
11	Larson, G., Rushmeier, H., & Piatko, C. (1997). A visibility matching tone reproduction operator for high dynamic range scenes. IEEE Trans. Vis. Comput. Graph.(3), pp. 291-306.
12	Philips, W., Shah, M., & Lobo, N. (2007). Flame recognition in video. Istanbul.
13	Reinhard, E., Stark, M., & Shirley, P. (2002). Photographic tone reproduction for digital images. ACM Trans. Graph.(21), pp. 267-276.
14	Ruzikulovich, T. (2022). Neyron tarmoq algoritmlari yordamida murakkab fondagi belgilarni aniqlash algoritmlari [Algorithms for detecting characters in complex backgrounds using neural network algorithms]. International Journal of Contemporary Scientific and Technical Research, pp. 238-241.
15	Tojiyev, M., Shirinboyev, R., & Sulaymonova, M. (2022). OpenCV kutubxonasida tasvirlarga rang modellari bilan ishlov berish [Processing images with color models in the OpenCV library]. Current Problems and Development Trends of modern Innovation Research: Solutions and Perspectives, 1(1), pp. 212-215.
16	Tojiyev, M., Ulug‘murodov, S., & Shirinboyev, R. (2022). Tasvirlar sifatini yaxshilashning chiziqli kontrast usuli [A linear contrast method for improving the quality of images]. Current Problems and Development Trends of Modern Innovation Research: Solutions and Perspectives, 1(1), pp. 215-217.
17	Toreyin, B., & Cetin, A. (2007). Online detection of fire in video. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-5. Minneapolis.
18	Tozhiyev, M., Primqulov, O., & Khasanov, D. (n.d.). Image segmentation in OpenCV and Python. doi:10.5958/2249-7137.2020.01735.8
19	Tumblin, J., & Rushmeier, H. (1993). Tone reproduction for realistic images. IEEE Comput. Graph. Appl.(13), pp. 42-48.
20	Xolboyevich, A. (2022). Pythonda chiziqli regressiya [Linear Regression in Python]. International Journal of Contemporary Scientific and Technical Research, pp. 233-238.
21	Zhang, J., Zhuang, J., & Du, H. (2006). A new flame detection method using probability model. Proceedings of the International Conference on Computational Intelligence and Security, pp. 1614–1617.