233
Views
0
CrossRef citations to date
0
Altmetric
Articles

A New Approach to Vision-based Fire and its Intensity Computation Using SPATIO-Temporal Features

, &

REFERENCES

  • T. Toulouse, X. Maldague, L. Rossi, M. Akhoufi, and T. Celik, “Benchmarking of wildland fire color segmentation algorithms,” IET Image Proc., Vol. 9, no. 12, pp. 1064–72, 2015. Available: https://ieeexplore.ieee.org/abstract/docu-ment/7332292.
  • M. P. Thompson, Y. Wei, D. E. Calkin, et al., “Risk management and analytics in wildfire response,” Curr. Forestry Rep., Vol. 5, pp. 226–39, 2019. DOI:10.1007/s40725-019-00101-7.
  • X. Hong, W. Wang, and Q. Liu. “Design and Realization of Fire Detection Using Computer Vision Technology,” 2019 Chinese Control And Decision Conference (CCDC), Nanchang, China, 2019, pp. 5645–9. Available: https://ieeexplore.ieee.org/document/8832897.
  • V. Pande, W. Elmannai, and K. Elleithy. “Classification and detection of fire on WSN using IMB400 multimedia sensor board,” 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, 2013, pp. 1–6. Available: https://ieeexplore.ieee.org/document/6578247.
  • S. Cao, D. Zhao, X. Liu, and Y. Sun, “Real-time robust detector for underwater live crabs based on deep learning,” Comput. Electron. Agric., Vol. 172 (2020). Available: https://www.sciencedirect.com/science/article/pii/S0168-16991931419X?via%3Dihub.
  • R.  Patel, K. Mandaliya, P. Shelar, R. Savani, and C. I. Patel, “Automatic fire detection using Combination of color Cue and flame flicker,” in Proceedings of the International Conference on Intelligent Systems and Signal Processing. advances in Intelligent Systems and computing, Vol. 671, R. Kher, D. Gondaliya, M. Bhesaniya, L. Ladid, and M Atiquzzaman, Eds. Singapore: Springer, 2018. Available: https://link.springer.com/chapter/10.1007/978-981-10-6977-2_3.
  • D. Pritam, and J. H. Dewan. “Detection of fire using image processing techniques with LUV color space,” 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, 2017, pp. 1158–62. Available: https://ieeexplore.ieee.org/document/8226309.
  • N. S. Bakri, R. Adnan, A. M. Samad, and F. A. Ruslan. A methodology for fire detection using color pixel classification,” 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), Batu Feringghi, 2018, pp. 94–98. Available: https://ieeexplore.ieee.org/document/8368692.
  • H. Yamagishi, and J. Yamaguchi. “Fire flame detection algorithm using a color camera,” MHS’99. Proceedings of 1999 International Symposium on Micromechatronics and Human Science (Cat. No.99TH8478), Nagoya, Japan, 1999, pp. 255-260. Available: https://ieeexplore.ieee.org/abstract/document/820014.
  • Tugar Celik, (2010). “Fast and efficient method for fire detection using image processing proceedings. ETRI Journal Volume 32 November 6. Available: https://onlinelib-rary.wiley.com/doi/abs/10.4218/etrij.10.0109.0695.
  • R. Di Lascio, A. Greco, A. Saggese, and M. Vento, “Improving fire detection reliability by a combination of videoanalytics,” in Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science, Vol. 8814, A. Campilho and M Kamel, Eds. Cham: Springer, 2014. Available: https://link.springer.com/chapter/10.1007/978-3-319-11758-4_52
  • K. Muhammad, J. Ahmad, Z. Lv, P. Bellavista, P. Yang, and S. W. Baik, “Efficient deep CNN-based fire detection and localization in video surveillance applications,” IEEE Transact. Syst., Man, and Cybernetics: Systems, Vol. 49, no.7, pp. 1419–34, 2019. Available: https://ieeexplore.ieee.org/document/8385121.
  • P. Foggia, A. Saggese, and M. Vento, “Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion,” IEEE Trans. Circuits Syst. Video Technol., Vol. 25, no. 9, pp. 1545–56, Sept. 2015. Available: https://ieeexplore.ieee.org/document/7014233.
  • X. Han, J. S. Jin, M. Wang, et al., “Video fire detection based on Gaussian mixture model and multi-color features,” SIViP, Vol. 11, pp. 1419–25, 2017. Available: https://link.springer.com/article/10.1007/s11760-017-1102-y.
  • B. Toptaş, and D. Hanbay, “A new artificial bee colony algorithm-based color space for fire/flame detection,” Soft. comput., Vol. 24, 2019.
  • S. Sahel, et al., “Logo detection using deep learning with pretrained CNN models,” Eng., Technol. Appl. Sci. Res., Vol. 11, no. 1, pp. 6724–9, 2021.
  • A. Kehili, Κ Dabbabi, and A. Cherif, “Early detection of Parkinson’s and Alzheimer’s diseases using the VOT_mean feature,” Eng., Technol. Applied ScienceResearch, Vol. 11, no. 2, pp. 6912–8, 2021.
  • R. Xu, et al., “A forest fire detection system based on ensemble learning,” Forests, Vol. 12, no. 2, pp. 217, 2021.
  • d. A. Pereira, G. Henrique, et al. “Active Fire Detection in Landsat-8 Imagery: a large-scale dataset and a deep-learning study.” arXiv e-prints (2021): arXiv-2101.
  • A. Shamsoshoara, et al., “Aerial imagery pile burn detection using deep learning: the FLAME dataset,” Comput. Netw., Vol. 193, pp. 108001, 2021.
  • C. Prema, S. Emmy, S. Vinsley, and S. Suresh, “Efficient flame detection based on static and dynamic texture analysis in forest fire detection,” Fire Technol., Vol. 54, no. 1, pp. 255–88, 2018.
  • K. Muhammad, J. Ahmad, and S. W. Baik, “Early fire detection using convolutional neural networks during surveillance for effective disaster management,” Neurocomputing, Vol. 288, pp. 30–42, 2018.
  • F. Gong, et al. “A real-time fire detection method from video with multifeature fusion.” Computational intelligence and neuroscience 2019 (2019).
  • M. Jeon, et al., “Multi-scale prediction for fire detection using convolutional neural network,” Fire Technol., Vol. 57, pp. 1–19, 2021.
  • P. J. Sunitha, and K. R. Joy. “Deep CNN-based fire alert system in Video Surveillance Networks.” Computational Vision and Bio-Inspired Computing. Springer, Singapore, 2021. 599–615.
  • G. Singh, N. Singh, and K. Kumar. ‘PICS: a novel technique for video summarization’, in Machine Intelligence and Signal Analysis, Singapore, 2019, pp. 411–21.
  • K. Kumar, D. D. Shrimankar, and N. Singh, “Eratosthenes sieve based key-frame extraction technique for event summarization in videos,” Multimed. Tools. Appl., Vol. 77, no. 6, pp. 7383–404, Mar. 2018.
  • K. Kumar, and D. D. Shrimankar, “F-DES: fast and deep event summarization,” IEEE Trans. Multimed., Vol. 20, no. 2, pp. 323–34, Feb. 2018.
  • K. Kumar, and D. D. Shrimankar, “Deep event learning boosT-up approach: DELTA,” Multimed. Tools. Appl., Vol. 77, no. 20, pp. 26635–55, Oct. 2018.
  • A. Khalil, et al., “Fire detection using multi color space and background modeling,” Fire Technol., Vol. 57, no. 3, pp. 1221–39, 2021.
  • J. Wang, Y. Lu, L. Gu, C. Zhou, and X. Chai, “Moving object recognition under simulated prosthetic vision using background-subtraction-based image processing strategies,” Inf. Sci. (NY), Vol. 277, pp. 512–24, 2014. ISSN 0020-0255.
  • K. Najeed, P. Hassan, A. Arsalan, and S. Aysha, “Unsupervised identification of malaria parasites using computer vision,” Pak. J. Pharm. Sci. (PJPS), Vol. 30, no. 1, pp. 223–8, January 2017.
  • N. A. Khan, U. Amin, Waseemullah, and M. Umer, “Unsupervised commercials identification in videos,” Int. J. Adv. Comput. Sci. Appl. (IJACSA), ISI. ISSN: 2156-5570, Vol. 8, no. 2, pp. 127–33, March 2017.
  • Waseemullah, N. A. Khan, and U. Amin. Unsupervised ads detection in TV transmissions. Int. J. Adv. Comput. Sci. Appl., ISI. ISSN: 2156-5570, May 2018.
  • M. A. A. Hamid, and N. A. Khan, “Investigation and classification of MRI brain tumors using feature extraction technique,” J. Med. Biol. Eng, Vol. 40, pp. 307–17, 2020. DOI:10.1007/s40846-020-00510-1.
  • Yaoming, Zhuang, et al. “Realization of moving object detection and tracking algorithm based on frame difference method and particle filter algorithm.” 2017 29th Chinese Control and Decision Conference (CCDC). IEEE, 2017.
  • L. Hu, and J. Cui, “Digital image recognition based on fractional-order-PCA-SVM coupling algorithm,” Measurement. (Mahwah. NJ), Vol. 145, pp. 150–9, 2019. ISSN 0263-2241, Available: https://www.sciencedirect.com/science/article/pii/S0263224119301162.
  • R. Di Lascio, A. Greco, A. Saggese, and M. Vento, “Improving fire detection reliability by a combination of videoanalytics,” in Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science, vol 8814, A. Campilho and M Kamel, Eds. Cham: Springer, 2014. Available: https://link.springer.com/chapter/10.1007/978-3-319-11758-4_52
  • K. Muhammad, J. Ahmad, Z. Lv, P. Bellavista, P. Yang and S. W. Baik, “Efficient deep CNN-based fire detection and localization in video surveillance applications,” in IEEE Trans. Syst., Man, Cybern.: Syst., Vol. 49, no. 7, pp. 1419–34, July 2019. Available: https://ieeexplore.ieee.org/document/8385121
  • X. Wu, X. Lu, and H. Leung. “An adaptive threshold deep learning method for fire and smoke detection,” 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, 2017, pp. 1954–9. Available: https://ieeexplore.ieee.org/document/8122904.
  • C. Stauffer, and W. E. L. Grimson. “Adaptive background mixture models for real-time tracking,” Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), Fort Collins, CO, USA, 1999, pp. 246–52 Vol. 2. Available: https://ieeexplore.ieee.org/document/784637.
  • R. Paugam, M. J. Wooster and G. Roberts, “Use of handheld thermal imager data for airborne mapping of fire radiative power and energy and flame front rate of spread,” in IEEE Trans. Geosci. Remote Sens., Vol. 51, no. 6, pp. 3385–99, June 2013. Available: https://ieeexplore.ieee.org/document/6377291.
  • videos, F. Fire Videos DB. Available: http://signal.ee.bilkent.edu.tr/VisiFire/Demo/FireClips/.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.