WANG Xiangjun, DU Zhiwei, GAO Chao. Small Scale Fire Identification Based on Constrained Inhomogeneous Deformation Feature[J]. Infrared Technology , 2021, 43(2): 145-152.
Citation: WANG Xiangjun, DU Zhiwei, GAO Chao. Small Scale Fire Identification Based on Constrained Inhomogeneous Deformation Feature[J]. Infrared Technology , 2021, 43(2): 145-152.

Small Scale Fire Identification Based on Constrained Inhomogeneous Deformation Feature

More Information
  • Received Date: October 10, 2019
  • Revised Date: December 29, 2020
  • Video based fire detection(VFD) is a convenient, low-cost method widely used in fire detection. However, it's not credible enough to distinguish true fire from possible disruptors by traditional fire features. This paper extract two new features to analyze the time series behavior of fire based on the motion of edge pixels. The inter frame behavior of edge pixels is regarded as a nonuniformty constrained deformation procedure. Combined with HMM and additional geometric features to distinguish true fire from possible disruptors, the accuracy of fire detection is greatly improved and the false alarm rateis efficiently reduced.
  • [1]
    Çetin A E, Dimitropoulos K, Gouverneur B, et al. Video fire detection -review[J]. Digital Signal Processing, 2013, 23(6): 1827-1843. DOI: 10.1016/j.dsp.2013.07.003
    [2]
    Owrutsky J C, Steinhurst D A, Minor C P, et al. Long wavelength video detection of fire in ship compartments[J]. Fire Safety Journal, 2006, 41(4): 315-320. DOI: 10.1016/j.firesaf.2005.11.011
    [3]
    Toreyin B U, Cinbis R G, Dedeoglu Y, et al. Fire detection in infrared video using wavelet analysis[J]. Optical Engineering, 2007, 46(6): 067204. DOI: 10.1117/1.2748752
    [4]
    李涛, 向涛, 黄仁杰, 等. 基于新的运动特征的火焰检测方法[J]. 计算机仿真, 2014, 31(9): 392-396. DOI: 10.3969/j.issn.1006-9348.2014.09.087

    LI Tao, XIANG Tao, HUANG Renjie, et al. Fire detection method based on new moving feature[J]. Computer Simulation, 2014, 31(9): 392-396. DOI: 10.3969/j.issn.1006-9348.2014.09.087
    [5]
    朱思思, 丁德红, 陈朝迎, 等. 基于图像处理的森林火灾识别方法研究[J]. 红外技术, 2016, 38(5): 440-446. http://hwjs.nvir.cn/article/id/hwjs201605014

    ZHU Sisi, DING Dehong, CHEN Zhaoying, et al. The research of forest fire recognition method based on image processing[J]. Infrared Technology, 2016, 38(5): 440-446. http://hwjs.nvir.cn/article/id/hwjs201605014
    [6]
    陈娟. 基于多特征融合的视频火焰探测方法研究[D]. 北京: 中国科学技术大学, 2009.

    CHEN Juan. Study on Method of Multi-Feature Fusion Based Video Flame Detection[D]. Beijing: University of Science and Technology of China, 2009.
    [7]
    CHEN J H, Im H G. Correlation of flame speed with stretch in turbulent premixed methane/air flames[J]. Symposium (International) on Combustion, 1997, 27(1): 819-826.
    [8]
    Hamins A, Yang J C, Kashiwagi T, An experimental investigation of the pulsation frequency of flames[J]. Symposium(International) on Combustion, 1992, 24(1): 1695-1702. http://www.sciencedirect.com/science/article/pii/S0082078406801980
    [9]
    Chaconmurguia M I, Perezvargas F J. Thermal video analysis for fire detection using shape regularity and intensity saturation features[C]//Mexican Conference on Pattern Recognition, 2011: 118-126.
    [10]
    Takahashi N, Suzuki M, Dobashi R, et al. Behavior of luminous zones appearing on plumes of large-scale pool fires of kerosene[J]. Fire Safety Journal, 1999, 33(1): 1-10. DOI: 10.1016/S0379-7112(99)00009-0
    [11]
    程鑫, 王大川, 尹东良. 图像型火灾火焰探测原理[J]. 火灾科学, 2005(4): 239-245, 196. https://www.cnki.com.cn/Article/CJFDTOTAL-HZKX200504007.htm

    CHENG Xin, WANG Dachuan, YIN Dongliang. Image type fire flame detecting principle[J]. Fire Safety Science, 2005(4): 239-245, 196. https://www.cnki.com.cn/Article/CJFDTOTAL-HZKX200504007.htm
    [12]
    Paresh P A, Parameswaran L. Vision-based algorithm for fire detection in smart buildings[C]//International Conference on ISMAC in Computational Vision and Bio-Engineering, 2018: 1029-1038.
    [13]
    Kegl B, Krzyzak A, Linder T, et al. A polygonal line algorithm for constructing principal curves[C]//Neural Information Processing Systems, 1998: 501-507.
    [14]
    WU Z, Fuller N W, Theriault D H, et al. A thermal infrared video benchmark for visual analysis[C]//Computer Vision and Pattern Recognition, 2014: 201-208.
  • Related Articles

    [1]GONG Jiamin, ZHANG Lei, LIU Shanghui, JIANG Jiewei, JIN Ku. Image Fusion Based on Simplified Two-Dimensional Kaniadakis Entropy Segmentation Algorithm and Fast Guided Filtering[J]. Infrared Technology , 2025, 47(2): 201-210.
    [2]JIANG Jiewei, LIU Shanghui, JIN Ku, LIU Haiyang, WEI Xumeng, GONG Jiamin. Infrared and Visible-Light Image Fusion Based on FCM and Guided Filtering[J]. Infrared Technology , 2023, 45(3): 249-256.
    [3]HU Jiahui, ZHAN Weida, GUI Tingting, SHI Yanli, GU Xing. Infrared Image Enhancement Method Based on Multiscale Weighted Guided Filtering[J]. Infrared Technology , 2022, 44(10): 1082-1088.
    [4]CHEN Wenyi, YANG Chengxun, YANG Hui. Multiscale Retinex Infrared Image Enhancement Based on the Fusion of Guided Filtering and Logarithmic Transformation Algorithm[J]. Infrared Technology , 2022, 44(4): 397-403.
    [5]CHENG Tiedong, LU Xiaoliang, YI Qiwen, TAO Zhengliang, ZHANG Zhizhao. Research on Infrared Image Enhancement Method Combined with Single-scale Retinex and Guided Image Filter[J]. Infrared Technology , 2021, 43(11): 1081-1088.
    [6]HUANG Zhihong, WU Sheng, XIAO Jian, ZHANG Keren, HUANG Wei. Thermal Fault Diagnosis of Power Equipments Based on Guided Filter[J]. Infrared Technology , 2021, 43(9): 910-915.
    [7]GE Peng, YANG Bo, HAN Qinglin, LIU Peng, CHEN Shugang, HU Douming, ZHANG Qiaoyan. Infrared Image Detail Enhancement Algorithm Based on Hierarchical Processing by Guided Image Filter[J]. Infrared Technology , 2018, 40(12): 1161-1169.
    [8]GAN Ling, ZHANG Qianwen. Image Fusion Method Combining Non-subsampled Contourlet Transform and Guide Filtering[J]. Infrared Technology , 2018, 40(5): 444-448,454.
    [9]GE Peng, YANG Bo, MAO Wenbiao, CHEN Shaolin, ZHANG Qiaoyan, HAN Qinglin. High Dynamic Range Infrared Image Enhancement Algorithm Based on Guided Image Filter[J]. Infrared Technology , 2017, 39(12): 1092-1097.
    [10]LIU Zhe, HAN jiuqiang, HUANG ShiQi. Single Image Super-Resolution Based on Multi-Guided Filtering[J]. Infrared Technology , 2017, 39(10): 920-927.
  • Cited by

    Periodical cited type(8)

    1. 朱亚辉. NSCT框架下动静态联合滤波的红外与可见光图像融合方法. 电脑知识与技术. 2024(08): 1-4 .
    2. 张剑,高云,何栋. 基于离散2-D小波多级分解的电容器外观缺陷视觉检测方法. 电子器件. 2024(05): 1255-1260 .
    3. 陈超洋,姜媛媛. 基于深度图像分解的红外与可见光图像融合. 红外技术. 2024(12): 1362-1370 . 本站查看
    4. 李晨,侯进,李金彪,陈子锐. 基于注意力与残差级联的红外与可见光图像融合方法. 计算机工程. 2022(07): 234-240 .
    5. 李文,叶坤涛,舒蕾蕾,李晟. 基于高斯模糊逻辑和ADCSCM的红外与可见光图像融合算法. 红外技术. 2022(07): 693-701 . 本站查看
    6. 李永萍,杨艳春,党建武,王阳萍. 基于变换域VGGNet19的红外与可见光图像融合. 红外技术. 2022(12): 1293-1300 . 本站查看
    7. 孙学蕾,高宏伟. 改进小波变换的红外与可见光融合方法研究. 沈阳理工大学学报. 2021(03): 19-23+28 .
    8. 赵汝海,汪方斌. 基于灰度和信息熵融合的金属疲劳偏振热像分割算法. 激光与光电子学进展. 2021(24): 260-271 .

    Other cited types(7)

Catalog

    Article views PDF downloads Cited by(15)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return