Small Scale Fire Identification Based on Constrained Inhomogeneous Deformation Feature

WANG Xiangjun; DU Zhiwei; GAO Chao

Volume 43 Issue 2

Mar. 2021

Turn off MathJax

Article Contents

Article Navigation > Infrared Technology > 2021 > 43(2): 145-152

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.

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.

PDF( 602 KB)

Small Scale Fire Identification Based on Constrained Inhomogeneous Deformation Feature

WANG Xiangjun^{1, 2
,},
DU Zhiwei^{1, 2},
GAO Chao³

1.
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
2.
MOEMS Education Ministry Key Laboratory, Tianjin University, Tianjin 300072, China
3.
College of Intelligence and Computing, Tianjin University, Tianjin 300072, China

Received Date: 2019-10-11
Rev Recd Date: 2020-12-30
Publish Date: 2021-02-20

Abstract

Abstract

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.
- fire detection,
- feature extraction,
- principal curves,
- HMM

FullText(HTML)

References(14)

References

[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.