A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 1
Jan.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
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

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
Funds:  This work was supported by National Natural Science Foundation of China (41471387, 41631072)
More Information
  • 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.

     

  • loading
  • [1]
    R. Bogue, " Sensors for fire detection,” Sens. Rev., vol. 33, no. 2, pp. 99–103, Mar. 2013. doi: 10.1108/02602281311299635
    [2]
    R. W. Fitzgerald, B. J. Meacham. Fire Detection. John Wiley & Sons, Ltd., 2017.
    [3]
    T. H. Chen, P. H. Wu, and Y. C. Chiou, " An early fire-detection method based on image processing,” in Proc. Int. Conf. Image Processing, Singapore, Singapore, 2004, pp. 1707–1710.
    [4]
    T. Çelik and H. Demirel, " Fire detection in video sequences using a generic color model,” Fire Saf. J., vol. 44, no. 22, pp. 147–158, Feb. 2009.
    [5]
    T. Wang, L. P. Bu, Q. F. Zhou, and Z. L. Yang, " A new fire recognition model based on the dispersion of color component,” in Proc. IEEE Int. Conf. Progress in Informatics and Computing, Nanjing, China, 2015, pp. 138–141.
    [6]
    R. Di Lascio, A. Greco, A. Saggese, and M. Vento, " Improving fire detection reliability by a combination of videoanalytics,” in Proc. 11th Int. Conf. Image Analysis and Recognition, Vilamoura, Portugal, 2014, pp. 477–484.
    [7]
    C. W. Chiu, T. Lu, H. T. Chao, and C. M. Shu, " Performance assessment of video-based fire detection system in tunnel environment,” Tunn. Undergr. Space Technol., vol. 40, pp. 16–21, Feb. 2014. doi: 10.1016/j.tust.2013.09.001
    [8]
    J. H. Zhang, J. Zhuang, H. F. Du, and S. A. Wang, " Flame detection algorithm based on video multi-feature fusion,” J. Xian Jiaotong Univ., vol. 40, no. 7, pp. 811–814, Jul. 2006.
    [9]
    S. D. Wang, Y. P. He, J. J. Zou, B. B. Duan, and J. Wang, " A flame detection synthesis algorithm,” Fire Technol., vol. 50, no. 4, pp. 959–975, Jul. 2014. doi: 10.1007/s10694-012-0321-6
    [10]
    A. Rafiee, R. Dianat, M. Jamshidi, R. Tavakoli, and S. Abbaspour, " Fire and smoke detection using wavelet analysis and disorder characteristics,” in Proc. 3rd Int. Conf. Computer Research and Development, Shanghai, China, 2011, pp. 262–265.
    [11]
    Y. H. Habiboğlu, O. Günay, and A. E. Çetin, " Covariance matrix-based fire and flame detection method in video,” Mach. Vis. Appl., vol. 23, no. 6, pp. 1103–1113, Nov. 2012. doi: 10.1007/s00138-011-0369-1
    [12]
    P. Foggia, A. Saggese, and M. Vento, " Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion,” IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 9, pp. 1545–1556, Sep. 2015. doi: 10.1109/TCSVT.2015.2392531
    [13]
    B. C. Ko, J. H. Jung, and J. Y. Nam, " Fire detection and 3D surface reconstruction based on stereoscopic pictures and probabilistic fuzzy logic,” Fire Saf. J., vol. 68, pp. 61–70, Aug. 2014. doi: 10.1016/j.firesaf.2014.05.015
    [14]
    S. Verstockt, S. van Hoecke, P. De Potter, P. Lambert, C. Hollemeersch, B. Sette, B. Merci, and R. van de Walle, " Multi-modal time-of-flight based fire detection,” Multimed. Tools Appl., vol. 69, no. 2, pp. 313–338, Mar. 2014. doi: 10.1007/s11042-012-0991-6
    [15]
    P. Gomes, P. Santana, and J. Barata, " A vision-based approach to fire detection,” Int. J. Adv. Robot. Syst., vol. 11, no. 9, pp. 149, Sep. 2014. doi: 10.5772/58821
    [16]
    T. Li, " Research on methods of multiple objects tracking in intelligent visual surveillance,” Ph.D. dissertation, USTC, Hefei, China, 2013.
    [17]
    A. Stadler, T. Windisch, and K. Diepold, " Comparison of intensity flickering features for video based flame detection algorithms,” Fire Saf. J., vol. 66, pp. 1–7, May 2014. doi: 10.1016/j.firesaf.2014.03.001
    [18]
    B. Lou, Y. Xu, and Z. G. Lin, " Image characteristic analysis of moving fire diffusion flame in circular motion,” J. Combust. Sci. Technol., vol. 19, no. 1, pp. 60–66, Feb. 2013.
    [19]
    L. Shi, F. F. Shi, T. Wang, L. P. Bu, and X. G. Hou, " A new fire detection method based on the centroid variety of consecutive frames,” in Proc. 2nd Int. Conf. Image, Vision and Computing, Chengdu, China, 2017.
    [20]
    S. Tulyakov, S. Jaeger, V. Govindaraju, and D. Doermann, " Review of classifier combination methods,” in Machine Learning in Document Analysis and Recognition, S. Marinai and H. Fujisawa, Eds. Berlin, Heidelberg, Germany: Springer, 2008, pp. 361–386.
    [21]
    J. Kittler, " Combining classifiers: a theoretical framework,” Pattern Anal. Appl., vol. 1, no. 1, pp. 18–27, Mar. 1998. doi: 10.1007/BF01238023
    [22]
    B. U. Töreyin, Y. Dedeoglu, U. Güdükbay, and A. E. Çetin, " Computer vision based method for real-time fire and flame detection,” Pattern Recognit. Lett., vol. 27, no. 1, pp. 49–58, Jan. 2006. doi: 10.1016/j.patrec.2005.06.015
    [23]
    A. E. Cetin, " Computer vision based fire detection software,” 2014. [Online]. Available: http://signal.ee.bilkent.edu.tr/VisiFire/
    [24]
    L. Lam and C. Y. Suen, " Optimal combinations of pattern classifiers,” Pattern Recognit. Lett., vol. 16, no. 9, pp. 945–954, Sep. 1995. doi: 10.1016/0167-8655(95)00050-Q

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(14)  / Tables(7)

    Article Metrics

    Article views (1148) PDF downloads(45) Cited by()

    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.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return