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Volume 7 Issue 1
Jan.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(1): 263-275
Teng Wang, Leping Bu, Zhikai Yang, Peng Yuan and Jineng Ouyang, "A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 263-275, Jan. 2020. doi: 10.1109/JAS.2019.1911546
Citation: Teng Wang, Leping Bu, Zhikai Yang, Peng Yuan and Jineng Ouyang, "A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 263-275, Jan. 2020. doi: 10.1109/JAS.2019.1911546
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A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment

doi: 10.1109/JAS.2019.1911546

  • Naval University of Engineering, College of Electrical Engineering Wuhan, China, 430022

Funds:  This work was supported by National Natural Science Foundation of China (41471387, 41631072)
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    Abstract
  • In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly, a multi-expert system consisting of color component dispersion, similarity and centroid motion is established to identify flames. The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.

     

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    Highlights

    • A new fire recognition model referring to the dispersion of fire color components is introduced in this paper. The threshold of Blue component standard deviation is calculated out by drawing the ROC curve of detecting results based on large number of sample images. A series of experiment results show that the proposed color model can eliminate the influence of common interferences and noises, and detect out suspected flame regions accurately in the image.
    • According to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed in this paper, which also can separate flame regions from interference areas. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established.
    • A multi-expert system consisting of color dispersion, similarity and centroid motion is established to identify flames. The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.

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