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Fire Detection Based on Fractal Analysis and Spatio-Temporal Features

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Abstract

Fire detection is one of the most important needs of surveillance and security systems in industrial applications. In this paper, a novel fire detection algorithm based on motion analysis using fractal and spatio-temporal features is presented. Initially, in each frame, dynamic textures are detected through three different fractal analysis methods and thresholding techniques. In the first method, Kernel Principal Component Analysis technique is used with fractal analysis and in the next a temporal blanket method is proposed. Finally, the third method is introduced based on temporal local fractal analysis and Laplace method. An RGB probability model is provided to separate the moving regions that have similar colors to the fire regions in each frame. Then, several spatio-temporal features such as correlation coefficient and mutual information are extracted from the candidate regions. Lastly, a two-class SVM classifier is used to classify these candidate regions. Various experimental results show that our proposed algorithm outperforms the relevant state-of-the-art algorithms.

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Authors and Affiliations

  1. Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Monir Torabian, Hossein Pourghassem & Homayoun Mahdavi-Nasab

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  1. Monir Torabian
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  2. Hossein Pourghassem
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  3. Homayoun Mahdavi-Nasab
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Correspondence to Hossein Pourghassem.

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Torabian, M., Pourghassem, H. & Mahdavi-Nasab, H. Fire Detection Based on Fractal Analysis and Spatio-Temporal Features. Fire Technol 57, 2583–2614 (2021). https://doi.org/10.1007/s10694-021-01129-7

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