آمار
| تعداد نشریات | 45 |
| تعداد شماره‌ها | 1,397 |
| تعداد مقالات | 17,043 |
| تعداد مشاهده مقاله | 54,969,098 |
| تعداد دریافت فایل اصل مقاله | 17,446,069 |
Outdoor fire detection on the video frames including fire zones close to the fire-like objects recorded by a fixed camera | ||
| مجله مهندسی برق دانشگاه تبریز | ||
| دوره 51، شماره 3 - شماره پیاپی 97، آبان 1400، صفحه 371-380 اصل مقاله (949.53 K) | ||
| نوع مقاله: علمی-پژوهشی | ||
| نویسندگان | ||
| محمود طالبیان مشهدی؛ فرحناز مهنا* | ||
| Department of Communication Engineering, University of Sistan and Baluchestan, Zahedan, Iran | ||
| چکیده | ||
| In this paper, an automatic outdoor fire detection method is proposed for the fire videos recorded by a fixed camera. First, a new set of color rules is introduced to eliminate the non-fire pixels as much as possible while detecting the fire zone pixels completely. Next, the texture and flicker effect features are extracted from the detected fire zone, to remove the remainder of non-fire pixels if still any non-fire pixel exists. The texture feature is extracted by using the angular second moment. To extract the flicker effect feature, the time prehistory signal of color components of each fire zone pixel is obtained and passed through a half band high pass filter. Finally, the Ward classifier clusters the fire features to separate the fire zone pixels from the non-fire. At the various steps of the proposed method, the morphology process is also used to improve the accuracy of fire detection. The proposed method is applied to the 200 different fire videos including the fire-like objects. Simulation results indicate 6% to 56% improvement on performance of the proposed method in comparison to the similar ones. | ||
| کلیدواژه‌ها | ||
| Fire detection؛ Outdoor fire video frames؛ Fixed camera؛ Feature extraction؛ Flicker effect feature؛ Clustering | ||
| مراجع | ||
|
[1] O. Gunay, K. Tasdemir, B. U. Toreyin, and A, E. Cetin, “Video based wildfire detection at night”, Fire Safety, Vol. 44, No. 6, pp. 860-868, 2009.
[2] J. Zhao et al., “SVM based forest fire detection using static and dynamic features”, Computer Science and Information Systems, Vol. 8, No. 3, pp. 821-841, 2011.
[3] A. Gupta, N. Bokde, D. Marathe and Kishore, “A novel approach for video based fire detection system using spatial and texture analysis”, Indian Journal of Science and Technology, Vol. 11, No. 19, pp. 1-17, 2018.
[4] B. Ugur Toreyin, Y. Dedeoglu, U. Gudukbay, and A. Enis Cetin, “Computer vision based method for real-time fire and flame detection”, Pattern Recognition Letters, Vol. 27, pp. 49-58, 2006. https://doi.org/10.1016/j.patrec.2005.06.015.
[5] D. H. Lee, S. H. Lee, T. Byun, N. IK. Cho, “Fire detection using color and motion models”, IEEE Transaction on Smart and Computing, Vol. 6, No. 4, pp. 237-245, 2017.
[6] F. Gong, C. Li, W. Gong, X. Li, X. Yuan, Y. Ma, and T. Song, “A real-time fire detection method from video with multifeature fusion”, Computational Intelligence and Neuroscience, Vol. 2019, No. 1, pp. 1-17, 2019.
[7] F. Karimi Zarchi, V. Derhami, A. Latif, and A. Ebrahimi, “Fire detection using video sequences in urban out-door environment”, Signal and Data Processing, Vol. 16, No. 3, pp. 61-78, 2019.
[8] A. Mouelhi, M. Bouchouicha, M. Sayadi, and E. Moreau, “Fire tracking in video sequences using geometric active contours controlled by artificial neural network”, 4th International Conference on Advanced Systems and Emergent Technologies, Tunisia, 2020. https://doi.org/10.1109/IC_ASET.2020.9318289
[9] M. Ajith, and M. Martinez-Ramon, “Unsupervised segmentation of fire and smoke from infra-red videos”, IEEE Access, Vol. 17, pp. 182381-182394, 2019. https://doi.org/10.1109/ACCESS.2019.2960209
[10] A. Gaur et al., “Fire Sensing Technologies: A Review”, IEEE Sensors, Vol. 19, No. 9, pp. 3191 – 3202, 2019. https://doi.org/10.1109/JSEN.2019.2894665
[11] X. Chen, A. Qing, K. Yu, and Y. Ban, “A novel fire identification algorithm based on improved color segmentation and enhanced feature data”, IEEE Transaction on Instrumentation and Measurement, Vol. 70, 2021. https://doi.org/10.1109/TIM.2021.3075380
[12] Y. Xie, J. Zhu, Y. Cao, Y. Zhang, D. Feng, Y. Zhang, and M. Chen, “Efficient video fire detection exploiting motion-flicker-based dynamic features and deep static features”, IEEE Access, Vol. 8, pp. 81904-81917, 2020. https://doi.org/10.1109/ACCESS.2020.2991338
[13] K. Muhammad, S. Khan, M. Elhoseny, S. H. Ahmed, and S. Wook Baik, “Efficient fire detection for uncertain surveillance environment”, IEEE Transaction on Industrial Informatics, Vol. 15, No. 5, pp. 3113-3122, 2019. https://doi.org/10.1109/TII.2019.2897594
[14] K. Muhammad, J. Ahmad, Z. Lv, P. Bellavista, P. Yang, S. Wook Baik, “Efficient deep CNN-based fire detection and localization in video surveillance applications”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 49, No. 7, pp. 1419 – 1434, 2018. https://doi.org/10.1109/TSMC.2018.2830099
[15] X. Wu, X. Lu, and H. Leung, “A video based fire smoke detection using robust AdaBoost”, Sensors, Vol. 18, No. 11, 3780, 2018.
[16] N. Randriamihamison, N. Vialaneix, and P. Neuvial, “Applicability and interpretability of Ward’s hierarchical agglomerative clustering with or without contiguity constraints”, Journal of Classification, Vol. 38, pp. 363-389, 2021.
[17] G. F. Shidik, F. N. Adnan, C. Supriyanto, and R. A. Pramunendar, and P. Andono, “Multi-color feature, background subtraction and time frame selection for fire detection”, Int. Conf. on Robotics, Biomimetic, Intelligent Computational Systems, 2013.
[18] V. Vipin, “Image processing based forest fire detection”, Int. Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 2, pp. 87-95, 2012.
[19] H. Jin, and R. B. Zhang, “A fire and flame detecting method based on video”, Int. Conf. on Machine Learning and Cybernetics, pp. 2347-2352, 2009. https://doi.org/10.1109/ICMLC.2009.5212165
[20] Ti Nguyen-Ti, Thuan Nguyen-Phuc, and Tuan Do-Hong, “Fire detection based on video processing method”, Int. Conf. on Advanced Technologies for Communications, pp. 106-110, 2013.
https://doi.org/10.1109/ATC.2013.6698087
| ||
|
آمار تعداد مشاهده مقاله: 393 تعداد دریافت فایل اصل مقاله: 433 |
||