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reflections, etc. Screenshots from these videos are
presented in Table 1 (the first column presents flame
detection results in real fire scenes, while the second
column contains screenshots from videos with fire
colored moving objects). Results are summarized in
Table 2 and Table 3 in terms of the true positive,
false negative, true negative and false positive ratios,
respectively.
Experimental results show that the proposed
method provides high detection rates in all videos
containing fire, with a reasonable false alarm ratio in
videos without fire. The high false positive rate in
“Non_fire_video3” is due to the continuous
reflections of car lights on the road, however, we
believe that the results may be improved in the
future with a better training of the SVM classifier.
The proposed method runs at 9 fps when the size of
the video sequences is 320x240. The experiments
were performed with a PC that has a Core 2 Quad
2.4 GHz processor with 3GB RAM. In the future,
the speed of the algorithm can be further improved
by dividing the image in blocks instead of using blob
analysis, which increases the processing time.
Table 2: Experimental results with videos containing fires.
Video Name
True Positive (%) False Negative (%)
Fire Video 1 98.89 1.11
Fire Video 2 93.46 6.54
Fire Video 3 99.59 0.41
Fire Video 4 99.03 0.97
Fire Video 5 90.00 10.0
Fire Video 6 99.50 0.50
Fire Video 7 99.59 0.41
Total 97.65 2.35
Table 3: Experimental results with videos containing fire
coloured objects.
Video Name
True Negative
(%)
False Positive
(%)
Non Fire Video 1 100.00 0.00
Non Fire Video 2 97.41 2.59
Non Fire Video 3 74.37 25.63
Non Fire Video 4 100.00 0.00
Non Fire Video 5 99.68 0.32
Non Fire Video 6 100.00 0.00
Non Fire Video 7 97.96 2.04
Total 98.01 1.99
4 CONCLUSIONS
Early detection of fire is crucial for the suppression
of wildfires and minimization of its effects. Video
based surveillance systems for automatic forest fire
detection is a promising technology that can provide
real-time detection and high accuracy. In this paper,
we presented a flame detection algorithm, which
identifies spatio-temporal features of fire such as
color probability, countour irregularity, spatial
energy, flickering and spatio-temporal energy. The
final decision is made by an SVM classifier, which
classifies candidate image regions as fire or non-fire.
The proposed technique was evaluated in a database
of 14 video sequences and demonstrated increased
detection accuracy.
ACKNOWLEDGEMENTS
The research leading to these results has received
funding from the European Community's Seventh
Framework Programme (FP7-ENV-2009-1) under
grant agreement no FP7-ENV-244088 ''FIRESENSE''.
We would like to thank all project partners for their
fruitful cooperation within FIRESENSE project.
REFERENCES
Ko, B. C., Ham, S. J., Nam, J. Y., Modeling and
Formalization of Fuzzy Finite Automata for Detection
of Irregular Fire Flames, IEEE Transactions on
Circuits and Systems for Video Technology, May
2011.
Stipaničev, D., Vuko, T., Krstinić, D., Štula, M., Bodrožić,
Lj., Forest Fire Protection by Advanced Video
Detection System - Croatian Experiences, Third
TIEMS Workshop – Improvement of Disaster
Management System, Trogir, 26-27 September, 2006
Töreyin, B., Dedeoglu, Y., Gudukbay, U., Cetin, A.,
"Flame detection in video using hidden markov
models," IEEE Int. Conf. on Image Processing, pp.
1230-1233, 2005.
Toreyin, B.U., Dedeoglu, Y., Gudukbay, U. Cetin, A.E.,
Computer vision based method for real-time fire and
flame detection, Pattern Recognition Letters, 27, pp.
49–58, 2006.
Zhang, Z., Zhao, J., Zhang, D., Qu, C., Ke, Y., and Cai,
B. “Contour Based Forest Fire Detection Using FFT
and Wavelet”, In Proceedings of CSSE (1). 2008, 760-
763.
Celik, T., Demirel, H., "Fire detection in video sequences
using a generic color model," Fire Safety Journal, Vol.
44, pp. 147-158, 2009.
Ko, B., Cheong, K., Nam, J., "Early fire detection
algorithm based on irregular patterns of flames and
hierarchical Bayesian Networks," Fire Safety Journal,
Vol 45, Issue 4, pp. 262-270, 2010.
Elgammal, A. Harwood, D., Davis L. “Non-parametric
model for background subtraction” 6th European
Conference on Computer Vision, Vol. 1843, pp. 751-
767, Dublin, Ireland, 2000.
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