Surveillance Video Fire Detection By Using Wavelet And Support Vector Machine
DOI:
https://doi.org/10.34818/eoe.v9i3.18144Referensi
[1] D. S. Riyadi and S. Aisyah, “Vision Based
Flame Detection System For Surveillance
Camera,” Proc. 2018 Int. Conf. Appl. Eng.
ICAE 2018, pp. 1–6, 2018, doi:
10.1109/INCAE.2018.8579405.
[2] K. Poobalan and S.-C. Liew, “Fire
Detection Algorithm Using Image,” no.
December, pp. 12–13, 2015, [Online].
Available: http://worldconferences.net.
[3] M. Adam, I. Mahmoud, and H. Ren,
“Forest Fire Detection and Identification
Using Image Processing and SVM,” vol.
15, no. 1, pp. 159–168, 2019.
[4] L. Shi, F. Long, Y. Zhan, and C. Lin,
“Video-based fire detection with spatiotemporal SURF and color features,” Proc.
World Congr. Intell. Control Autom., vol.
2016-Septe, pp. 258–262, 2016, doi:
10.1109/WCICA.2016.7578269.
[5] Y. K. Singh and D. Deb, “Detection of fire
regions from a video image frames in
YCbCr color model,” Int. J. Recent
Technol. Eng., vol. 8, no. 3, pp. 6082–
6086, 2019, doi:
10.35940/ijrte.C5637.098319.
[6] X. Han, L. Xiao, J. S. Jin, M. W. Wei, and
J. Lei, “Video fire detection based on
Gaussian Mixture Model and multi-color
features,” Signal, Image Video Process.,
2017, doi: 10.1007/s11760-017-1102-y.
[7] D. K. Appana, R. Islam, S. A. Khan, and J.
M. Kim, “A video-based smoke detection
using smoke flow pattern and spatialtemporal energy analyses for alarm
systems,” Inf. Sci. (Ny)., vol. 418–419, pp.
91–101, 2017, doi:
10.1016/j.ins.2017.08.001.
[8] S. Wilson, S. P. Varghese, G. A. Nikhil, I.
Manolekshmi, and P. G. Raji, “A
Comprehensive Study on Fire Detection,”
Proc. IEEE Conf. Emerg. Devices Smart
Syst. ICEDSS 2018, no. March, pp. 242–
246, 2018, doi:
10.1109/ICEDSS.2018.8544329.
[9] L. Debnath and F. A. Shah, Lecture Notes
on Wavelet Transforms, 1st ed. Birkhäuser,
Cham, 2017.
[10] C. L. Wang, R. H. Hwang, T. S. Chen, and
H. Y. Lee, “Detecting and restoring system
of tampered image based on discrete
wavelet transformation and block
truncation coding,” Proc. - Int. Conf. Adv.
Inf. Netw. Appl. AINA, vol. 2, no. 1, pp.
79–82, 2005, doi: 10.1109/AINA.2005.160.
[11] M. Kociolek, A. Materka, M. Strzelecki,
and P. Szczypinski, “Discrete Wavelet
Transform – Derived Features for Digital
Image Texture Analysis,” Int. Conf. Signals
Electron. Syst., no. September, pp. 163–
168, 2001.
[12] A. Hyvärinen, J. Hurri, and P. O. Hoyer,
Natural Image Statistics-A Probabilistic
Approach to Early Computational Vision.,
vol. 39. 2009.
[13] V. Kecman, “Support Vector Machines –
An Introduction,” no. May, pp. 1–47, 2005,
doi: 10.1007/10984697_1.
[14] V. Jakkula, “Tutorial on Support Vector
Machine (SVM),” Sch. EECS, Washingt.
State Univ., pp. 1–13, 2011, [Online].
Available:
http://www.ccs.neu.edu/course/cs5100f11/r
esources/jakkula.pdf.
[15] K. M. Ting, “Precision and Recall,” in
Encyclopedia of Machine Learning, C.
Sammut and G. I. Webb, Eds. Boston, MA:
Springer US, 2010, p. 781.



