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Covariance matrix-based fire and flame detection method in video

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Abstract

This paper proposes a video-based fire detection system which uses color, spatial and temporal information. The system divides the video into spatio-temporal blocks and uses covariance-based features extracted from these blocks to detect fire. Feature vectors take advantage of both the spatial and the temporal characteristics of flame-colored regions. The extracted features are trained and tested using a support vector machine (SVM) classifier. The system does not use a background subtraction method to segment moving regions and can be used, to some extent, with non-stationary cameras. The computationally efficient method can process 320 × 240 video frames at around 20 frames per second in an ordinary PC with a dual core 2.2 GHz processor. In addition, it is shown to outperform a previous method in terms of detection performance.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Bilkent University, Ankara, 06800, Turkey

    Yusuf Hakan Habiboğlu, Osman Günay & A. Enis Çetin

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  1. Yusuf Hakan Habiboğlu
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  2. Osman Günay
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  3. A. Enis Çetin
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Correspondence to Osman Günay.

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Habiboğlu, Y.H., Günay, O. & Çetin, A.E. Covariance matrix-based fire and flame detection method in video. Machine Vision and Applications 23, 1103–1113 (2012). https://doi.org/10.1007/s00138-011-0369-1

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  • DOI: https://doi.org/10.1007/s00138-011-0369-1

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