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Computer vision, fire detection, image analysis, local binary patterns, image segmentation
Techniques to detect the flame at an early stage are necessary in order to prevent the fire and minimize the damage. The flame detection technique based on the physical sensor has limited disadvantages in detecting the fire early. This paper presents the results of using local binary patterns for solving flames detecting problem and proposes modifications to improve the quality of detector work. Experimentally found that using support vector machines classifier with a kernel based on Gaussian radial basis functions shows the best results compared to other SVM cores or classifier k-nearest neighbors.
Oleksii Maksymiv, Taras Rak, Dmytro Peleshko,"Video-based Flame Detection using LBP-based Descriptor: Influences of Classifiers Variety on Detection Efficiency", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.2, pp.42-48, 2017. DOI:10.5815/ijisa.2017.02.06
T. Chen, P. Wu, and Y. Chiou, “An early fire-detection method based on image processing,” ICIP ’04, pp. 1707–1710, 2004.
X. Qi, J. Ebert “A Computer Vision-Based Method for Fire Detection in Color Videos,” International journal of imaging, no. 9, pp. 22–34, 2009.
Celik T., H. Demirel “Fire detection in video sequences using a generic color model,” Fire Safety Journal, no.2, pp. 147–158, 2009.
S. Rinsurongkawong, M. Ekpanyapong, and M. N. Dailey, “Fire detection for early fire alarm based on optical flow video processing,” in 9th Int. Conf. on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology Phetchaburi, pp. 1-4, 2012.
B.U. Toreyin, Y. Dedeogln, U. Gudukbay, and A.E. Cetin , “Computer vision based method for real time fire and flame detection,” Pattern Recognition Letters, vol. 27, no. 4, pp. 49-58, 2006.
L.Ying, W. Hui-qin, B. Zhen-Bo and X. Fei “Research on Large Space Building Fire Positioning Technology Using Video Surveillance Images,”pp. 1218–1222, 2013.
P. Gomes, P. Santana, J. Barata “Vision-based Approach to Fire Detection,” International Journal of Advanced Robotic Systems, no. 11, pp. 1–12, 2014.
Jiang, B., Lu, Y., Li, X. and Lin, L. “Towards a solid solution of realtime fire and flame detection,” Multimedia Tools and Applications., 2014.
F. Sthevanie, H. Nugroho, F. Arif Yulianto “Visual-Based Fire Detection Using Local Binary Pattern-Three Orthogonal Planes,” CYBERNETICSCOM, 2013, pp. 155–159.
Y. Zhao, T. Guizhong “Fire video recognition based on flame and smoke characteristics”, Systems and Informatics (ICSAI), 2014, pp. 113 – 118.
T. Ojala, M. Pietikainen, and T. Maenpaa “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971–987, 2002.
S. Nigam, A. Khare “Integration of moment invariants and uniform local binary patterns for human activity recognition in video sequences,” Multimed Tools Appl, 2015, pp. 1–30.
S. Liao, M. W. “Dominant Local Binary Patterns for Texture Classification,” Transactions on Image Processing, Vol 18, 2009, No 5
T. Celik “Fast and efficient method for fire detection using image processing,” ETRI journal, 2010, №6, pp. 881–890.
T. Celik, H. Ozkaramanli, H. Demirel “Fire pixel classification using fuzzy logic and statistical color model,” Acoustics, Speech and Signal Processing, 2007, pp. 1205–1208.
H. Tian, L. Wanqing, P. Ogunbona and others “Smoke Detection in Videos Using Non-Redundant Local Binary Pattern-Based Features,” Multimedia Signal Processing, 2010, pp. 1–4.
D. T. Nguyen, Z. Zong, P. Ogunbona, and W. Li, “Object detection using non-redundant local binary patterns,” in Proc. IEEE International Conference on Image Processing, 2010, pp. 4609–4612.
G. Zhao and M. Pietikäinen, "Dynamic Texture Recognition Using Volume Local Binary Patterns," Infotech Oulu and Department of Electrical and Information Engineering University of Oulu, Oulu, 2007.
J. C. Burges. “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2(2), pp. 121-167, 1998.
U. Krebel “Pairwise classification and support vector Machines. In Advances in Kernel Methods: Support Vector Learnings,” MIT Press, Cambridge, MA, pp 255-268, 1999.
Jinho Kim, Byeong-soo Kim and Silvio Savarese, “Comparison Image Classification Methods: K-Nearest Neighbor and Support-Vector-Machines”, Proceedings of the 6th WSEAS International Conference on Circuits, Systems, Signal and Telecommunications”, Cambridge, USA, pp. 133-138, Jan. 25-27, 2012.
O. Maksymiv, T. Rak, O. Menshikova, "Deep convolutional network for detecting probable emergency situations", 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, pp. 199-202, 2016.
M. Pietikäinen, A. Hadid, G. Zhao and T. Ahonen, “Computer Vision Using Local Binary Patterns,” Oulu: Springer, 2011
B. Scholkopf and A. J. Smola. “Learning with Kernels,” MIT Press, 2002.
Ye zhiwei, Yang Juan, Zhang Xu, Hu Zhengbing,"Remote Sensing Textual Image Classification based on Ensemble Learning", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.12, pp.21-29, 2016.DOI: 10.5815/ijigsp.2016.12.03.