Video-based Flame Detection using LBP-based Descriptor: Influences of Classifiers Variety on Detection Efficiency

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Oleksii Maksymiv 1,* Taras Rak 1 Dmytro Peleshko 2

1. Lviv State University of Life Safety, Lviv, Ukraine

2. Lviv Polytechnic National University, Lviv, Ukraine

* Corresponding author.


Received: 1 Apr. 2016 / Revised: 1 Jul. 2016 / Accepted: 14 Aug. 2016 / Published: 8 Feb. 2017

Index Terms

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.

Cite This Paper

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


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