Abstract
In this paper we propose a novel method for detecting fires in both indoor and outdoor environments. The videos acquired by traditional surveillance cameras are analyzed and different typologies of information, respectively based on color and movement, are combined into a multi expert system in order to increase the overall reliability of the approach, making it possible its usage in real applications. The proposed algorithm has been tested on a very large dataset acquired in real environments and downloaded on the web. The obtained results confirm a consistent reduction in the number of false positive detected by the system, without paying in terms of accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Celik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Safety Journal 44(2), 147–158 (2009)
Celik, T., Demirel, H., Ozkaramanli, H., Uyguroglu, M.: Fire detection using statistical color model in video sequences. J. Vis. Comun. Image Represent. 18(2), 176–185 (2007). http://dx.doi.org/10.1016/j.jvcir.2006.12.003
Cetin, A.E., Dimitropoulos, K., Gouverneur, B., Grammalidis, N., Gunay, O., Habiboglu, Y.H., Toreyin, B.U., Verstockt, S.: Video fire detection: a review. Digital Signal Processing 23(6), 1827–1843 (2013)
Cetin, E.: Computer vision based fire detection dataset (May 2014), http://signal.ee.bilkent.edu.tr/VisiFire/
Conte, D., Foggia, P., Petretta, M., Tufano, F., Vento, M.: Meeting the application requirements of intelligent video surveillance systems in moving object detection. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 653–662. Springer, Heidelberg (2005)
Lam, L., Suen, C.Y.: Optimal combinations of pattern classifiers. Pattern Recognition Letters 16(9), 945–954 (1995)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Mivia: Mivia fire detection dataset (May 2014), http://mivia.unisa.it/
Qi, X., Ebert, J.: A computer vision-based method for fire detection in color videos. International Journal of Imaging 2(9 S), 22–34 (2009)
Rafiee, A., Tavakoli, R., Dianat, R., Abbaspour, S., Jamshidi, M.: Fire and smoke detection using wavelet analysis and disorder characteristics. In: IEEE ICCRD, vol. 3, pp. 262–265 (March 2011)
Ravichandran, A., Soatto, S.: Long-range spatio-temporal modeling of video with application to fire detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 329–342. Springer, Heidelberg (2012)
Shi, J., Tomasi, C.: Good features to track. In: IEEE CVPR, pp. 593–600 (1994)
Töreyin, B.U., Dedeoğlu, Y., Güdükbay, U., Çetin, A.E.: Computer vision based method for real-time fire and flame detection. Pattern Recogn. Lett. 27(1), 49–58 (2006)
Yu, C., Mei, Z., Zhang, X.: A real-time video fire flame and smoke detection algorithm. Procedia Engineering 62, 891–898 (2013). asia-Oceania Symposium on Fire Science and Technology
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Di Lascio, R., Greco, A., Saggese, A., Vento, M. (2014). Improving Fire Detection Reliability by a Combination of Videoanalytics. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8814. Springer, Cham. https://doi.org/10.1007/978-3-319-11758-4_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-11758-4_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11757-7
Online ISBN: 978-3-319-11758-4
eBook Packages: Computer ScienceComputer Science (R0)