Fire Detection in Video Stream by Using Simple Artificial Neural Network
This paper deals with the preliminary research of the fire detection in a video stream. Early fire detection can save lives and properties from huge losses and damages. Therefore the surveillance of the areas is necessary. Early fire discovery with high accuracy, i.e. a low number of false positive or false negative cases, is essential in any environment, especially in places with the high motion of people. The traditional fire detection sensors have some drawbacks: they need separate systems and infrastructure to be implemented, to use sensors in the case of the industrial environment with open fire technologies is often impossible, and others. The fire detection in a video stream is one of the possible and feasible solutions suitable for replacement or supplement of conventional fire detection sensors without a need for installation a huge infrastructure. The paper provides the state of the art in the fire detection. The following part of the paper proposes the new system of feature extraction and describes the feedforward neural network which was used for the training and testing of the proposed idea. The promising results are presented with over 93% accuracy on a selected dataset of movies which consist of more and highly varied instances than published by other researchers involved in the fire detection field. The structure of the neural networks promises higher computational speed than currently implemented deep learning systems.
Ha, C., Hwang, U., Jeon, G., Cho, J., Jeong, J.: Vision-Based Fire Detection Algorithm Using Optical Flow. In: 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems. pp. 526–530 (2012).
Tripathi, A.K., Swarup, S.: Visual smoke detection. Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma. 10116 LNCS, 128–142 (2017).
Khatami, A., Mirghasemi, S., Khosravi, A., Nahavandi, S.: A New Color Space Based on K-Medoids Clustering for Fire Detection. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics. pp. 2755–2760 (2015).
Khatami, A., Mirghasemi, S., Khosravi, A., Nahavandi, S.: An efficient hybrid algorithm for fire flame detection. In: 2015 International Joint Conference on Neural Networks (IJCNN). pp. 1–6 (2015).
Wang, T., Shi, L., Yuan, P., Bu, L., Hou, X.: A new fire detection method based on flame color dispersion and similarity in consecutive frames. In: 2017 Chinese Automation Congress (CAC). pp. 151–156 (2017).
Abdullahi, Z.S., Dalhatu, S.H., Abdullahi, Z.H.: Development of Fire Detection Algorithm at Its Early Stage Using Fire Colour and Shape Information. IOP Conf. Ser. Mater. Sci. Eng. 344, 012021 (2018).
Li, S., Liu, W., Ma, H., Fu, H.: Multi-attribute based fire detection in diverse surveillance videos. Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma. 10132 LNCS, 238–250 (2017).
Vijayalakshmi, S.R., Muruganand, S.: Fire alarm based on spatial temporal analysis of fire in video. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC). pp. 104–109 (2018).
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 Recognit. Image Anal. 27, 131–138 (2017).
Wattanachote, K., Li, L., Lin, Z., Wang, G., Jiang, M., Liu, W.: An investigation on motion periodic series for fire and smoke textures characteristic identification. In: 2017 10th International Conference on Ubi-media Computing and Workshops (Ubi-Media). pp. 1–6 (2017).
Töreyin, B.U., Dedeoğlu, Y., Güdükbay, U., Çetin, A.E.: Computer vision based method for real-time fire and flame detection. Pattern Recognit. Lett. 27, 49–58 (2006).
Mao, W., Wang, W., Dou, Z., Li, Y.: Fire Recognition Based On Multi-Channel Convolutional Neural Network. Fire Technol. 54, 531–554 (2018).
Zhong, Z., Wang, M., Shi, Y., Gao, W.: A convolutional neural network-based flame detection method in video sequence. Signal Image Video Process. 12, 1619–1627 (2018).
Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux, J., Moreau, E., Fnaiech, F.: Convolutional neural network for video fire and smoke detection. In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. pp. 877–882 (2016).
Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., Baik, S.W.: Convolutional Neural Networks Based Fire Detection in Surveillance Videos. IEEE Access. 6, 18174–18183 (2018).
Hüttner, V., Steffens, C.R., Botelho, S.S. da C.: First response fire combat: Deep leaning based visible fire detection. In: 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR). pp. 1–6 (2017).
Shen, D., Chen, X., Nguyen, M., Yan, W.Q.: Flame detection using deep learning. In: 2018 4th International Conference on Control, Automation and Robotics (ICCAR). pp. 416–420 (2018).
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the theory of neural computation. Addison-Wesley/Addison Wesley Longman, Reading, MA, US, ISBN 978-0-201-50395-1 (1991).
Wasserman, P.D.: Neural Computing: Theory and Practice. Coriolis Group, New York, ISBN 978-0-442-20743-4 (1989).
Fausett, L.V.: Fundamentals of Neural Networks: Architectures, Algorithms And Applications. Pearson, Englewood Cliffs, NJ, ISBN 978-0-13-334186-7 (1993).
Jarusek, R., Volna, E., Kotyrba, M.: Robust steganographic method based on unconventional approach of neural networks. Appl. Soft Comput. 67, 505–518 (2018).
Rosebrock, A.: Deep learning for computer vision with Python. PyImageSearch, ISBN 978-1-986538-13-8.
Stanford University CS231n: Convolutional Neural Networks for Visual Recognition, http://cs231n.stanford.edu/.
MENDEL open access articles are normally published under a Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ . Under the CC BY-NC-SA 4.0 license permitted 3rd party reuse is only applicable for non-commercial purposes. Articles posted under the CC BY-NC-SA 4.0 license allow users to share, copy, and redistribute the material in any medium of format, and adapt, remix, transform, and build upon the material for any purpose. Reusing under the CC BY-NC-SA 4.0 license requires that appropriate attribution to the source of the material must be included along with a link to the license, with any changes made to the original material indicated.