Fast fire flame recognition algorithm based on multi-feature logarithmic regression

doi:10.11772/j.issn.1001-9081.2017.07.1989

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (7): 1989-1993.DOI: 10.11772/j.issn.1001-9081.2017.07.1989

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Fast fire flame recognition algorithm based on multi-feature logarithmic regression

XI Tingyu¹, QIU Xuanbing¹, SUN Dongyuan¹, LI Ning¹, LI Chuanliang¹, WANG Gao², YAN Yu³

1. School of Applied Science, Taiyuan University of Science and Technology, Taiyuan Shanxi 030024, China;
2. National Defense Key Laboratory for Electronic Test Technology, North University of China, Taiyuan Shanxi 030051, China;
3. Automobile Engineering Department, Hebei College of Industry and Technology, Shijiazhuang Hebei 050091, China

Received:2016-12-26 Revised:2017-02-26 Online:2017-07-10 Published:2017-07-18
Supported by:
This work is partially supported by the National Natural Science Foundation of China (U1610117, 11504256, 61573323), the National College Students Innovation and Entrepreneurship Training Program of China (2016264), Scientific and Technological Innovation Program of Higher Education Institutions in Shanxi (2015166).

多特征量对数回归的火焰快速识别算法

席廷宇¹, 邱选兵¹, 孙冬远¹, 李宁¹, 李传亮¹, 王高², 鄢玉³

1. 太原科技大学应用科学学院, 太原 030024;
2. 中北大学电子测试技术国防科技重点实验室, 太原 030051;
3. 河北工业职业技术学院汽车工程系, 石家庄 050091

通讯作者: 邱选兵
作者简介:席廷宇(1995-),男,山西运城人,硕士研究生,主要研究方向:嵌入式系统、图像处理;邱选兵(1980-),男,四川内江人,副教授,博士,主要研究方向:激光光谱、嵌入式系统;孙冬远(1992-),男(满族),河北承德人,硕士研究生,主要研究方向:激光光谱、嵌入式系统;李宁(1994-),男,河北邯郸人,主要研究方向:嵌入式系统;李传亮(1983-),男,山东淄博人,副教授,博士,主要研究方向:激光光谱;王高(1973-),男,山西侯马人,教授,博士,主要研究方向:高温测量;鄢玉(1979-),女,四川资阳人,讲师,硕士,主要研究方向:汽车电子。
基金资助:
国家自然科学基金资助项目（U1610117，11504256，61573323）；国家级大学生创新创业训练计划项目（2016264）；山西省高校科技创新项目（2015166）。

Abstract

Abstract: To improve the recognition rate and reduce the false-recognition rate in real-time detection of flame in video surveillance, a fast flame recognition algorithm based on multi-feature logarithm regression model was proposed. Firstly, the image was segmented according to the chromaticity of the flame, and the Candidate Fire Region (CFR) was obtained by subtracting the moving target image with reference image. Secondly the features of the CRF such as area change rate, circularity, number of sharp corners and centroid displacement were extracted to establish the logarithmic regression model. Then, a total of 300 images including flame and non-flame images, which were got from National Institute of Standards and Technology (NIST), Computer Vision laboratory of Inha University (ICV), Fire detection based on computer Vision (VisiFire) and the experimental library consisting of the candle and paper combustion were used to parametric learning. Finally, 8 video clips including 11071 images were used to validate the proposed algorithm. The experimental results show that the True Positive Rate (TPR) and True Negative Rate (TNR) of the proposed algorithm are 93% and 98% respectively. The average time of identification is 0.058 s/frame. Because of its fast identification and high recognition rate, the proposed algorithm can be applied in embedded real-time flame image recognition.

Key words: flame recognition, multi-feature, logarithmic regression, embedded video, real-time fire alarm

摘要： 为了提高实时视频监控中火焰识别率和降低误识率，提出了一种基于多特征量对数回归模型的火焰快速识别算法。首先，根据火焰的色度特征进行图像分割，通过运动目标与参考图像差分运算获取火焰候选区域（CFR）；然后提取候选区域的面积变化率、圆形度、尖角个数以及质心位移等特征量，建立火焰的对数回归快速识别模型；其次采用美国国家标准与技术研究院（NIST）、仁荷大学计算机视觉实验室（ICV）和基于计算机视觉的火灾探测（VisiFire）实验库以及自制蜡烛、纸燃烧火焰中的火焰和非火焰图像中的300幅进行参数学习；最后选取实验数据库中8段视频共11071幅图像进行识别算法检验。测试结果表明，所提算法的真正率（TPR）达到93%、真负率（TNR）达到98%，识别平均用时0.058 s/帧。所提算法识别速度快且识别率高，可以应用于嵌入式实时图像火焰识别。

关键词: 火焰识别, 多特征量, 对数回归, 嵌入式视频, 实时火灾预警

CLC Number:

XI Tingyu, QIU Xuanbing, SUN Dongyuan, LI Ning, LI Chuanliang, WANG Gao, YAN Yu. Fast fire flame recognition algorithm based on multi-feature logarithmic regression[J]. Journal of Computer Applications, 2017, 37(7): 1989-1993.

席廷宇, 邱选兵, 孙冬远, 李宁, 李传亮, 王高, 鄢玉. 多特征量对数回归的火焰快速识别算法[J]. 计算机应用, 2017, 37(7): 1989-1993.

References

[1] 黄正宇,缪小平,芮挺.基于图像的火焰检测中无量纲动态特征研究[J].计算机应用,2012,32(7):1894-1898.(HUANG Z Y, MIAO X P, REI T. Study on dimensionless dynamic characteristics of flame based on image recognition[J]. Journal of Computer Applications, 2012, 32(7):1894-1898.)
[2] 毛文涛,王文朋,蒋梦雪,等.基于局部特征过滤的快速火焰图像识别方法[J].计算机应用,2016,36(10):2907-2911.(MAO W T, WANG W P, JIANG M X, et al. Fast flame recognition approach based on local feature filtering[J]. Journal of Computer Applications, 2016, 36(10):2907-2911.)
[3] KO B C, CHEONG K H, NAM J Y. Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian networks[J]. Fire Safety Journal, 2010, 45(4):262-270.
[4] WANG Y, YU C, TU R, et al. Fire detection model in Tibet based on grey-fuzzy neural network algorithm[J]. Expert Systems with Applications, 2011, 38(8):9580-9586.
[5] WANG L, YE M, DING J, et al. Hybrid fire detection using hidden Markov model and luminance map[J]. Computers & Electrical Engineering, 2011, 37(6):905-915.
[6] TRUONG T X, KIM J M. Fire flame detection in video sequences using multi-stage pattern recognition techniques[J]. Engineering Applications of Artificial Intelligence, 2012, 25(7):1365-1372.
[7] WANG D C, CUI X, PARK E, et al. Adaptive flame detection using randomness testing and robust features[J]. Fire Safety Journal, 2013, 55:116-125.
[8] ÇELIK T, DEMIREL H. Fire detection in video sequences using a generic color model[J]. Fire Safety Journal, 2009, 44(2):147-158.
[9] HABIBOGLU Y H, GUNAY O, ÇETIN A E. Covariance matrix-based fire and flame detection method in video[J]. Machine Vision and Applications, 2012, 23(6):1103-1113.
[10] BOSCH I, GOMEZ S, MOLINA R, et al. Object discrimination by infrared image processing[C]//Proceedings of the 2009 International Work-Conference on the Interplay between Natural and Artificial Computation. Berlin:Springer, 2009:30-40.
[11] DIMITROPOULOS K, TSALAKANIDOU F, GRAMMALIDIS N. Flame detection for video-based early fire warning systems and 3D visualization of fire propagation[C]//CGIM 2012:Proceedings of the 13th IASTED International Conference on Computer Graphics and Imaging. Calgary, CA:ACTA Press. 2012:3-13.
[12] HORNG W B, PENG J W. A fast image-based fire flame detection method using color analysis[J]. Tamkang Journal of Science and Engineering, 2008, 11(3):273-285.
[13] 吴冬梅,杨娟利,王静.基于Ohta颜色空间的火焰检测[J].电视技术,2016,40(6):140-143.(WU D M, YANG J L, WANG J. Flame recognition based on Ohta color space[J]. Video Engineering, 2016, 40(6):140-143.)
[14] 张霞,黄继风.结合LBP直方图和SVM的视频火焰检测[J].计算机应用与软件,2016,33(8):216-220.(ZHANG X, HUANG J F. Detecting flame in video in combination with LBP HISTOGRAM and SVM[J]. Computer Applications and Software, 2016, 33(8):216-220.)
[15] 刘尚旺,段德全,崔艳萌,等.二次定位车牌分割及识别方法[J].河南师范大学学报(自然科学版),2016,44(4):151-156.(LIU S W, DUAN D Q, CUI Y M, et al. Vehicle license plate segmentation and recognition method based on two times locations[J]. Journal of Henan Normal University (Natural Science Edition), 2016, 44(4):151-156.)
[16] BORGES P V K, IZQUIERDO E. A probabilistic approach for vision-based fire detection in videos[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2010, 20(5):721-731.

Fast fire flame recognition algorithm based on multi-feature logarithmic regression

多特征量对数回归的火焰快速识别算法

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