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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Saturday 24 August 2019

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Entropy Functional Based Auto Adaptive Wildfire Detection Using Fuzzy Logic


Volume 10, Issue 1, October 2014, Pages 19–26

 Entropy Functional Based Auto Adaptive Wildfire Detection Using Fuzzy Logic

S. Poonguzhali, Anuradha Nischal, and T. Gomathi

Original language: English

Received 5 August 2014

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Nowadays digital camera technology and video processing techniques are increased worldwide. Due to this, the conventional fire detection methods are going to be replaced by computer vision based systems. The computer vision based systems detection has a significant role with surveillance system. Most of the algorithms used in the existing techniques propose spectral, spatial, temporal and other low level features of fire for distinguishing it from other objects in video sequences. This paper proposes a new approach to computational vision-based fire and flame detection by using a fuzzy logic edge detection and motion detection with ANN-SVM classifier as classification tool. The edge detection using fuzzy canny edge detection technique and the motion detection using motion estimation are use for fire and flame detection and ANN-SVM classifier is useful for the final classification. Finally, it decided whether the objects that have changed in that video are flame or not. Therefore, this method detects both smoke and flame effectively and obtain high accuracy by reduce false alarm rate.

Author Keywords: Fuzzy canny edge detection, motion estimation, wildfire detection using video, SVM-ANN classifier, features extraction.


How to Cite this Article


S. Poonguzhali, Anuradha Nischal, and T. Gomathi, “Entropy Functional Based Auto Adaptive Wildfire Detection Using Fuzzy Logic,” International Journal of Innovation and Scientific Research, vol. 10, no. 1, pp. 19–26, October 2014.