Algorithm for forest fire smoke detection in video

  • Rykhard P. Bohush Polotsk State University, 29 Blachina Street, Navapolack 211440, Belarus https://orcid.org/0000-0002-6609-5810
  • Sergey V. Ablameyko Belarusian State University, 4 Niezaliežnasci Avenue, Minsk 220030, Belarus; United Institute of Informatics Problems, National Academy of Sciences of Belarus, 6 Surhanava Street, Minsk 220012, Belarus https://orcid.org/0000-0001-9404-1206
DOI: https://doi.org/10.33581/2520-6508-2021-1-91-101

Abstract

In this paper, an efficient forest smoke detection algorithm in video sequences obtained from a stationary camera is proposed. The algorithm composed of three basic steps. At the first step, the frame contrast is improved. After that detection of slowly moving areas is performed based on dynamic and static features. For this we use adaptive background subtraction and color segmentation. The detected areas are divided into small blocks. Spatio-temporal analysis is applied to them. Blocks are classified based on covariance descriptors and support vector machine with a radial basis kernel function. Experimental results for processing real video show effectiveness of our algorithm for early forest smoke detection.

Author Biographies

Rykhard P. Bohush, Polotsk State University, 29 Blachina Street, Navapolack 211440, Belarus

PhD (engineering), docent; head of the department of computer systems and networks, faculty of information technology

Sergey V. Ablameyko, Belarusian State University, 4 Niezaliežnasci Avenue, Minsk 220030, Belarus; United Institute of Informatics Problems, National Academy of Sciences of Belarus, 6 Surhanava Street, Minsk 220012, Belarus

academician of the National Academy of Sciences of Belarus, doctor of science (engineering), full professor; professor at the department of web-technologies and computer simulation, faculty of mechanics and mathematics, Belarusian State University, and chief researcher at the department of intelligent information systems, National Academy of Sciences of Belarus

References

  1. Kudrin AYu, Zaporozhets AI, Podrezov YuV. [Modern methods of detecting and monitoring forest fires]. Tekhnologii grazhdanskoi bezopasnosti. 2006;3(4):66–67. Russian.
  2. Baranovsky OV, Krasnoproshin VV, Valvachev AN. System of monitoring natural and territorial complexes. Vestnik of Brest State Technical University. Physics, mathematics, informatics. 2019;5:12–15. Russian.
  3. Ye S, Bai Z, Chen H, Bohush R, Ablameyko S. An effective algorithm to detect both smoke and flame using color and wavelet analysis. Pattern Recognition and Image Analysis. 2017;27(1):131–138. DOI: 10.1134/S1054661817010138.
  4. Gomez-Rodriguez F, Arrue BС, Ollero A. Smoke monitoring and measurement using image processing: application to forest fires. In: Sadjadi FA, editor. Automatic target recognition XIII. AeroSense; 2003 April 21–25; Orlando, Florida, USA. [S. l.]: [s. n.]; 2003. p. 404–411. (Proceedings of SPIE; volume 5094). DOI: 10.1117/12.487050.
  5. Vicente J, Guillemant P. An image processing technique for automatically detecting forest fire. International Journal of Thermal Sciences. 2002;41(12):1113–1120. DOI: 10.1016/S1290-0729(02)01397-2.
  6. Toreyin BU, Cetin AE. Wildfire detection using LMS based active learning. In: IEEE International conference on acoustics, speech and signal processing; 2009 April 19–24; Taipei, Taiwan. [S. l.]: Institute of Electrical and Electronics Engineers; 2009. p. 1461–1464. DOI: 10.1109/ICASSP.2009.4959870.
  7. Genovese A, Labati RD, Piuri V, Scotti F. Wildfire smoke detection using computational intelligence techniques. IEEE International conference on computational intelligence for measurement systems and applications (CIMSA) proceedings; 2011 September 19–21; Ottawa, Canada. [S. l.]: Institute of Electrical and Electronics Engineers; 2011. p. 1–6. DOI: 10.1109/CIMSA.2011.6059930.
  8. Hakan Habiboglu Y, Gunay O, Cetin AE. Real-time wildfire detection using correlation descriptors. In: Mestre X, Hernando J, Pardas M, editors. 19th European signal processing conference (EUSIPCO-2011); 2011 August 29 – September 2; Barcelona, Spain. Kessariani: European Association for Signal, Speech, and Image Processing (EURASIP); 2011. p. 894–898. (Proceedings of EUSIPCO; volume 19).
  9. Ko BC, Park JO, Nam J-Y. Spatiotemporal bag-of-features for early wildfire smoke detection. Image and Vision Computing. 2013;31(10):786–795. DOI: 10.1016/j.imavis.2013.08.001.
  10. Luxing Qin, Xuehui Wu, Yichao Cao, Xiaobo Lu. An effective method for forest fire smoke detection. Journal of Physics: Conference Series. 2019;1187(5):052045. DOI: 10.1088/1742-6596/1187/5/052045.
  11. Khomyakova VV, Khomyakov AN. Convolutional neural network for video-based smoke detection. In: Loginov YuYu, editor. Reshetnevskie chteniya. Chast’ 2. Materialy XXII Mezhdunarodnoi nauchno-prakticheskoi konferentsii, posvyashchennoi pamyati general’nogo konstruktora raketno-kosmicheskikh sistem akademika M. F. Reshetneva; 12–16 noyabrya 2018 g.; Krasnoyarsk, Rossiya [Reshetnev readings. Part 2. Materials of the 22nd International scientific and practical conference dedicated to the memory of the general designer of the rocket and space systems, academician M. F. Reshetnev; 2018 November 12–16; Krasnoyarsk, Russia]. Krasnoyarsk: Reshetnev Siberian State University of Science and Technology; 2018. p. 308–309. Russian.
  12. Zhao Y, Zhang H, Zhang X, Qian W. Wildfire smoke detection based on depthwise separable convolutions and target-awareness. Preprint 2020040027. 2020 April 3. DOI: 10.20944/preprints202004.0027.v1.
  13. Zivkovic Z, van der Heijden F. Recursive unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004;26(5):651–656. DOI: 10.1109/TPAMI.2004.1273970.
  14. Vapnik VN, Chervonenkis AYa. Teoriya raspoznavaniya obrazov (statisticheskie problemy obucheniya) [Pattern recognition theory (statistical learning problems)]. Moscow: Nauka; 1974. 416 p. Russian.
Published
2021-04-12
Keywords: forest fire, image analysis, background, covariance descriptors, support vector machine
How to Cite
Bohush, R. P., & Ablameyko, S. V. (2021). Algorithm for forest fire smoke detection in video. Journal of the Belarusian State University. Mathematics and Informatics, 1, 91-101. https://doi.org/10.33581/2520-6508-2021-1-91-101
Issue
No 1 (2021): Journal of the Belarusian State University. Mathematics and Informatics
Section
Theoretical Foundations of Computer Science

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