https://oldena.lpnu.ua/handle/ntb/52546| Title: | Core Generator of Hypotheses for Real-Time Flame Detecting |
| Authors: | Peleshko, Dmytro Maksymiv, Oleksii Rak, Taras Voloshyn, Orysia Morklianyk, Bohdan |
| Affiliation: | IT Step University Lviv National Polytechnic University Lviv State University of Life Safety |
| Bibliographic description (Ukraine): | Core Generator of Hypotheses for Real-Time Flame Detecting / Dmytro Peleshko, Oleksii Maksymiv, Taras Rak, Orysia Voloshyn, Bohdan Morklianyk // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 455–458. — (Machine Vision and Pattern Recognition). |
| Bibliographic description (International): | Core Generator of Hypotheses for Real-Time Flame Detecting / Dmytro Peleshko, Oleksii Maksymiv, Taras Rak, Orysia Voloshyn, Bohdan Morklianyk // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 455–458. — (Machine Vision and Pattern Recognition). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | computer vision machine learning flame detection color segmentation |
| Number of pages: | 4 |
| Page range: | 455-458 |
| Start page: | 455 |
| End page: | 458 |
| Abstract: | The flame is a visually unstable and constantly changeable process, which causes considerable difficulties for its detection in the video streams. Although the modern architecture of convolutional neural networks can show high accuracy, their integration into real-time systems is problematic, because they require a large amount of computing resources. To reduce the number of these resources, it is proposed to select possible regions of interest (ROI), which are based on the developed generator of hypotheses. Compared to existing flame detection algorithms, the developed generator of hypotheses allows you to work with the minimum of computing resources and has a high degree of classification completeness due to improved methods of color segmentation and moving objects detection. |
| URI: | https://ena.lpnu.ua/handle/ntb/52546 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] P. Patel, and S. Tiwari, "Flame Detection using Image Processing Techniques", Int. J. Comput. Applic., vol. 58, no. 18, pp. 13-16, 2012. [2] Thou-Ho Chen, Cheng-Liang Kao, and Sju-Mo Chang, "An intelligent real-time fire-detection method based on video processing," IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, 2003. Proceedings., pp. 104-111, 2003. [3] Norsyahirah Izzati binti Zaidi, Nor Anis Aneza binti Lokman, Mohd Razali bin Daud, Hendriyawan Achmad and Khor Ai Chia, "Fire recognition using RGB and YCBCR color space," ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 21, pp. 9786-9790, November 2015. [4] Turgay Çelik, Hüseyin Özkaramanlı, and Hasan Demirel, "Fire and smoke detection without sensors: image processing based approach,"15th European Signal Processing Conference, pp. 1794-1798, 2007. [5] L. Rossi, M. Akhloufi, and Y. Tison, "On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics," Fire Safety Journal, vol. 46, no. 1-2, pp. 9-20, 2011. [6] C. Emmy Prema, S. S. Vinsley, and S. Suresh, "Multi Feature Analysis of Smoke in YUV Color Space for Early Forest Fire Detection," Fire Technology, vol. 52, no. 5, p. 1319–1342, 2016. [7] Thou-Ho (Chao-Ho) Chen, Ping-Hsueh Wu, and Yung-Chuen Chiou, "An Early Fire-Detection Method Based on Image Processing," in Proc. Int. Conf. Image Process. (ICIP), vol. 3., pp. 1707–1710, Oct. 2004. [8] Guruh Fajar Shidik, Fajrian Nur Adnan, Catur Supriyanto, Ricardus Anggi Pramunendar, and Pulung Nurtantio Andono, "Multi color feature, background subtraction and time frame selection for fire detection," 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems, pp. 115-120, 2013. [9] T. Celik, "Fast and efficient method for fire detection using image processing," ETRI journal, vol. 32, no. 6, pp. 881-890, 2010. [10] Suchet Rinsurongkawong, Mongkol Ekpanyapong, and Matthew N. Dailey., "Fire detection for early fire alarm based on optical flow video processing," 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 1-4, 2012 . [11] B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, "Wavelet based realtime smoke detection in video," Signal Processing Conference, 13th European, pp. 1-4, 2005. [12] Zhao-Guang Liu, Yang Yang, and Xiu-Hua Ji, "Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space," Signal, Image and Video Processing, vol. 10, no. 2, pp. 277-284, 2016. [13] B. Toreyin, Y. Dedeoglu, A. Cetin, "Flame detection in video using hidden markov models," IEEE International Conference on Image Processing, vol. 2, pp. 213-216, 2005. |
| References (International): | [1] P. Patel, and S. Tiwari, "Flame Detection using Image Processing Techniques", Int. J. Comput. Applic., vol. 58, no. 18, pp. 13-16, 2012. [2] Thou-Ho Chen, Cheng-Liang Kao, and Sju-Mo Chang, "An intelligent real-time fire-detection method based on video processing," IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, 2003. Proceedings., pp. 104-111, 2003. [3] Norsyahirah Izzati binti Zaidi, Nor Anis Aneza binti Lokman, Mohd Razali bin Daud, Hendriyawan Achmad and Khor Ai Chia, "Fire recognition using RGB and YCBCR color space," ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 21, pp. 9786-9790, November 2015. [4] Turgay Çelik, Hüseyin Özkaramanlı, and Hasan Demirel, "Fire and smoke detection without sensors: image processing based approach,"15th European Signal Processing Conference, pp. 1794-1798, 2007. [5] L. Rossi, M. Akhloufi, and Y. Tison, "On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics," Fire Safety Journal, vol. 46, no. 1-2, pp. 9-20, 2011. [6] C. Emmy Prema, S. S. Vinsley, and S. Suresh, "Multi Feature Analysis of Smoke in YUV Color Space for Early Forest Fire Detection," Fire Technology, vol. 52, no. 5, p. 1319–1342, 2016. [7] Thou-Ho (Chao-Ho) Chen, Ping-Hsueh Wu, and Yung-Chuen Chiou, "An Early Fire-Detection Method Based on Image Processing," in Proc. Int. Conf. Image Process. (ICIP), vol. 3., pp. 1707–1710, Oct. 2004. [8] Guruh Fajar Shidik, Fajrian Nur Adnan, Catur Supriyanto, Ricardus Anggi Pramunendar, and Pulung Nurtantio Andono, "Multi color feature, background subtraction and time frame selection for fire detection," 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems, pp. 115-120, 2013. [9] T. Celik, "Fast and efficient method for fire detection using image processing," ETRI journal, vol. 32, no. 6, pp. 881-890, 2010. [10] Suchet Rinsurongkawong, Mongkol Ekpanyapong, and Matthew N. Dailey., "Fire detection for early fire alarm based on optical flow video processing," 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 1-4, 2012 . [11] B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, "Wavelet based realtime smoke detection in video," Signal Processing Conference, 13th European, pp. 1-4, 2005. [12] Zhao-Guang Liu, Yang Yang, and Xiu-Hua Ji, "Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space," Signal, Image and Video Processing, vol. 10, no. 2, pp. 277-284, 2016. [13] B. Toreyin, Y. Dedeoglu, A. Cetin, "Flame detection in video using hidden markov models," IEEE International Conference on Image Processing, vol. 2, pp. 213-216, 2005. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Peleshko_D-Core_Generator_of_Hypotheses_455-458.pdf | 332.17 kB | Adobe PDF | View/Open | |
| 2018_Peleshko_D-Core_Generator_of_Hypotheses_455-458__COVER.png | 517.47 kB | image/png | View/Open |
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