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Received：23 August 2017，
Revised：2017-10-7，
Published：16 March 2018
移动端阅览
目的
2
视频烟雾检测具有响应速度快、不易受环境因素影响、适用面广、成本低等优势，为及早预警火灾提供有力保障。近年涌现大量视频检测方法，尽管检测率有所提升，但仍受到高误报率和高漏报率的困扰。为了全面反映视频烟雾检测的研究现状和最新进展，本文重点针对2014年至2017年国内外公开发表的主要文献，进行全面的梳理和分析。
方法
2
该工作建立在广泛文献调研的基础上，立足于视频烟雾检测的基本框架，围绕视频图像预处理、疑似烟区提取、烟雾特征描述、烟雾分类识别等处理阶段，系统地对最新文献进行分析和总结。此外，对区别于传统框架的深度学习检测方法亦进行了相关归纳。
结果
2
重点依据烟雾运动特征和烟雾静态特征这两类，对疑似烟区提取方法进行梳理；从统计量特征、变换域特征和局部模式特征3个方面对烟雾特征描述方法进行梳理，并从颜色、形状等七个角度进行总结；从基于规则和基于学习这两个视角，梳理烟雾识别和决策方法；最后，对于基于深度学习的方法单独进行了阐述。文献通过系统地梳理，凝练出视频烟雾检测近几年取得的进展和尚存在的不足，并对视频烟雾检测发展前景进行展望。
结论
2
针对视频烟雾检测的研究一直备受青睐，越来越多性能优秀的检测算法不断涌现。通过对现有研究进行全面梳理和系统分析，期望视频烟雾检测能取得更大的进展并更好地应用于工业领域，为火灾预警提供更有力的保障。
Objective
2
Video smoke detection methods can guarantee real-time fire alarms because these methods respond quickly to fire and have strong robustness to the environment
suitability for various scenes
and low-cost application. Many state-of-the-art video smoke detection methods have been proposed recently. The detection rates of these methods have been greatly improved by recent efforts
but these methods still suffer from the problem of high false and missing alarms. We provide an up-to-date critical survey of research on video smoke detection methods to keep up with the latest research progress
research focus
and development trends in video smoke detection. We focused on domestic and international research on video smoke detection published from 2014 to 2017. These publications include feature extraction
smoke recognition
and detection based on images and videos.
Method
2
We review papers on video smoke detection and summarize a general research framework for video smoke detection. The general framework of video smoke detection indicates that the general procedure of these methods is divided into several processing steps
namely
video preprocessing
detection of candidate smoke regions
feature extraction of smoke regions
video smoke classification
and other processing techniques. We discuss these methods in detail according to the general processing steps. Aside from describing traditional smoke detection methods based on handcrafted features
we also discuss and analyze deep learning-based smoke detection methods that were recently proposed given that deep learning is a hot area in machine learning research.
Result
2
On the basis of the general processing steps
we analyze these video preprocessing methods and divide the relevant literature into three major categories. These video preprocessing methods include preprocessing methods for color
preprocessing techniques for noise interference
and preprocessing approaches to image segmentation. Candidate smoke regions are detected in two ways. One way is to simply divide the image into a set of blocks
and each image block is tested. Some blocks may be classified as a candidate smoke region. Another way is to extract complete candidate smoke regions. The extraction methods of complete candidate smoke regions are sorted according to smoke motion and static characteristics. The information of smoke motion features can be obtained through object detection based on background modeling technology and object detection based on the simple differences of adjacent frames.Some static characteristics of smoke can be summed up as traditional features
such as color and shape. Other static characteristics are extracted by methods of a novel perspective. Statistical measures
transformation domain
and local features are classified into seven categories of features:color
shape
gradient
orientation
textures
frequency
and motion. Classification methods for video smoke detection are first reviewed and then categorized into two types
namely
rule-based methods and learning-based ones. Deep learning-based methods are reviewed independently because they are different from traditional smoke recognition methods. On the basis of the above analysis of these methods
we detail the advantages of existing video smoke detection methods. State-of-the-art methods with high detection rates and low false alarm rates have been proposed in recent years. Some novel methods exist. Some methods attempt to explore classification methods. Some datasets have been created and widely used to facilitate the training and testing of methods. We also elaborate the shortcomings of existing video smoke detection methods. The false alarm rates and error rates of detection methods remain high. Most algorithms are dependent on scenes and are easily disturbed by noise. Smoke features are simply combined without rules in some methods. Moreover
no publicly available video datasets exist with labeled smoke regions and standard evaluation criteria for video smoke detection. Finally
we suggest possible promising directions for future research. First
a set of video datasets is manually labeled
and standard evaluation criteria must be established. Second
researchers must explore essential features of smoke. Third
an effective fusing method for multiple smoke features extracted by different methods is needed. Fourth
smoke features must be automatically learned by machine learning methods instead of handcrafted designed ones. Finally
we refer to a new detection framework
such as deep learning-based frameworks
which are completely different from the basic framework.
Conclusion
2
Video smoke detection methods are one of the most important and popular research topics nowadays. Our review and analysis of existing methods may provide researchers with powerful support for their work on early fire alarms
promote the advancement of video smoke detection
and further push industrial applications for video smoke detection.
Fire Department of Ministry of Public Security. Fire disaster analysis of 2016[EB/OL]. 2017-07-08[2017-06-22] . http://www.119.gov.cn/xiaofang/nbnj/34601.htm http://www.119.gov.cn/xiaofang/nbnj/34601.htm .
公安部消防局. 2016年全国火灾情况分析[EB/OL]. 2017-07-08[2017-06-22] . http://www.119.gov.cn/xiaofang/nbnj/34601.htm http://www.119.gov.cn/xiaofang/nbnj/34601.htm .
Luo S, Jiang Y Z.State-of-art of video based smoke detection algorithms[J]. Journal of Image and Graphics, 2013, 18(10):1225-1236.
罗胜, Jiang Y Z.视频检测烟雾的研究现状[J].中国图象图形学报, 2013, 18(10):1225-1236.[DOI:10.11834/jig.20131002]
Ojo J A, Oladosu J A. Video-based smoke detection algorithms:a chronological survey[J]. Computer Engineering and Intelligent Systems, 2014, 5(7):38-50.
Vicente J, Guillemant P. An image processing technique for automatically detecting forest fire[J]. International Journal of Thermal Sciences, 2002, 41(12):1113-1120.[DOI:10.1016/S1290-0729(02)01397-2]
Guillemant P, Vicente J. Real-time identification of smoke images by clustering motions on a fractal curve with a temporal embedding method[J]. Optical Engineering, 2001, 40(4):554-563.[DOI:10.1117/1.1355254]
Kopilovic I, Vagvolgyi B, Szirányi T. Application of panoramic annular lens for motion analysis tasks: surveillance and smoke detection[C]//Proceedings of the 15th International Conference on Pattern Recognition. Barcelona, Spain: IEEE, 2000: 714-717. [ DOI: 10.1109/ICPR.2000.903017 http://dx.doi.org/10.1109/ICPR.2000.903017 ]
Toreyin B U, Dedeoglu Y, Cetin A E. Contour based smoke detection in video using wavelets[C]//Proceedings of the 14th European Signal Processing Conference. Florence, Italy: IEEE, 2006: 1-5.
Tung T X, Kim JM. An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems[J]. Fire Safety Journal, 2011, 46(5):276-282.[DOI:10.1016/j.firesaf.2011.03.003]
Grech-Cini H J. Smoke Detection: US, 6844818[P]. 2005-01-18.
Yuan F N. Motion accumulation and translucence based model for video smoke detection[J]. Journal of Data Acquisition&Processing, 2007, 22(4):396-400.
袁非牛.基于运动累积和半透明的视频烟雾探测模型[J].数据采集与处理, 2007, 22(4):396-400.[DOI:10.3969/j.issn.1004-9037.2007.04.002]
Yuan F N. A fast accumulative motion orientation model based on integral image for video smoke detection[J]. Pattern Recognition Letters, 2008, 29(7):925-932.[DOI:10.1016/j.patrec.2008.01.013]
Yuan F N, Zhang Y M, Liu S X, et al. Video smoke detection based on accumulation and main motion orientation[J]. Journal of Image and Graphics, 2008, 13(4):808-813.
袁非牛, 张永明, 刘士兴, 等.基于累积量和主运动方向的视频烟雾检测方法[J].中国图象图形学报, 2008, 13(4):808-813.[DOI:10.11834/jig.20080434]
Chen T H, Yin Y H, Huang S F, et al. The smoke detection for early fire-alarming system base on video processing[C]//Proceedings of 2006 International Conference on Intelligent Information Hiding and Multimedia. Pasadena, CA, USA: IEEE, 2006: 427-430. [ DOI: 10.1109/ⅡH-MSP.2006.265033 http://dx.doi.org/10.1109/ⅡH-MSP.2006.265033 ]
Krstinić D, Stipaničev D, Jakovčević T. Histogram-based smoke segmentation in forest fire detection system[J]. Information Technology and Control, 2009, 38(3):237-244.
Fujiwara N, Terada K. Extraction of a smoke region using fractal coding[C]//2004 IEEE International Symposium on Communications and Information Technology. Sapporo, Japan: IEEE, 2004: 659-662. [ DOI: 10.1109/ISCIT.2004.1413797 http://dx.doi.org/10.1109/ISCIT.2004.1413797 ]
Gubbi J, Marusic S, Palaniswami M. Smoke detection in video using wavelets and support vector machines[J]. Fire Safety Journal, 2009, 44(8):1110-1115.[DOI:10.1016/j.firesaf.2009.08.003]
Tian H D, Li W Q, Wang L, et al. A novel video-based smoke detection method using image separation[C]//Proceedings of 2012 IEEE International Conference on Multimedia and Expo. Melbourne, VIC, Australia: IEEE, 2012: 532-537. [ DOI: 10.1109/ICME.2012.72 http://dx.doi.org/10.1109/ICME.2012.72 ]
Verstockt S, Van Hoecke S, Beji T, et al. A multi-modal video analysis approach for car park fire detection[J]. Fire Safety Journal, 2013, 57:44-57.[DOI:10.1016/j.firesaf.2012.07.005]
Yuan F N. Video-based smoke detection with histogram sequence of LBP and LBPV pyramids[J]. Fire Safety Journal, 2011, 46(3):132-139.[DOI:10.1016/j.firesaf.2011.01.001]
Yuan F N. A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection[J]. Pattern Recognition, 2012, 45(12):4326-4336.[DOI:10.1016/j.patcog.2012.06.008]
Qureshi W S, Ekpanyapong M, Dailey M N, et al. QuickBlaze:early fire detection using a combined video processing approach[J]. Fire Technology, 2016, 52(5):1293-1317.[DOI:10.1007/s10694-015-0489-7]
Cruz H, Eckert M, Meneses J, et al. Efficient forest fire detection index for application in unmanned aerial systems (UASs)[J]. Sensors, 2016, 16(6):893.[DOI:10.3390/s16060893]
Jia Y, Yuan J, Wang J J, et al. A saliency-based method for early smoke detection in video sequences[J]. Fire Technology, 2016, 52(5):1271-1292.[DOI:10.1007/s10694-014-0453-y]
Li S, Shi Y S, Wang B, et al. Video smoke detection based on color transformation and MSER[J]. Transactions of Beijing Institute of Technology, 2016, 36(10):1072-1078.
李笋, 石永生, 汪渤, 等.基于颜色增强变换和MSER检测的烟雾检测算法[J].北京理工大学学报, 2016, 36(10):1072-1078.[DOI:10.15918/j.tbit1001-0645.2016.10.016]
Ye S P, Bai Z C, Chen H F, et al. An effective algorithm to detect both smoke and flame using color and wavelet analysis[J]. Pattern Recognition and Image Analysis, 2017, 27(1):131-138.[DOI:10.1134/S1054661817010138]
Liu Q S, Cui G S, Wu P, et al. Remove of fix interference based on machine learning in smoke detection[J]. Computer Measurement&Control, 2015, 23(3):880-881, 885.
刘青松, 崔更申, 吴鹏, 等.基于机器学习的烟雾检测算法去除固定干扰[J].计算机测量与控制, 2015, 23(3):880-881, 885.[DOI:10.3969/j.issn.1671-4598.2015.03.059]
Ince I F, Yildirim M E, Salman Y B, et al. Fast video fire detection using luminous smoke and textured flame features[J]. KSⅡ Transactions on Internet and Information Systems, 2016, 10(12):5485-5506.[DOI:10.3837/tiis.2016.12.019]
Wang Y B. Smoke detection based on computer vision in coal mine[J]. Journal of Liaoning Technical University:Natural Science, 2016, 35(11):1230-1234.
王媛彬.煤矿烟雾的计算机视觉检测方法[J].辽宁工程技术大学学报:自然科学版, 2016, 35(11):1230-1234.[DOI:10.11956/j.issn.1008-0562.2016.11.007]
Pal S K, King R A. On edge detection of x-ray images using fuzzy sets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983, 5(1):69-77.[DOI:10.1109/TPAMI.1983.4767347]
Li X L, Song W G, Lian L P, et al. Forest fire smoke detection using back-propagation neural network based on modis data[J]. Remote Sensing, 2015, 7(4):4473-4498.[DOI:10.3390/rs70404473]
Liu Y, Gu X D, Du J L, et al. Fire smoke detection method based on multi-feature running average method[J]. Video Engineering, 2016, 40(9):95-99, 121.
刘颖, 顾小东, 杜久玲, 等.基于多特征融合运行期均值法的烟雾检测算法[J].电视技术, 2016, 40(9):95-99, 121.[DOI:10.16280/j.videoe.2016.09.019]
Chen J Z, Wang Z J, Chen H H, et al. Dynamic smoke detection using cascaded convolutional neural network for surveillance videos[J]. Journal of University of Electronic Science and Technology of China, 2016, 46(6):992-996.
陈俊周, 汪子杰, 陈洪瀚, 等.基于级联卷积神经网络的视频动态烟雾检测[J].电子科技大学学报, 2016, 46(6):992-996.[DOI:10.3969/j.issn.1001-0548.2016.06.020]
Tong B B, Wang S T. Video smoke detection usingtwo-level classification algorithm[J]. Computer Science, 2015, 42(3):301-306.
仝伯兵, 王士同.用两层分类算法进行视频烟雾检测[J].计算机科学, 2015, 42(3):301-306.[DOI:10.11896/j.issn.1002-137X.2015.03.062]
Dimitropoulos K, Barmpoutis P, Grammalidis N. Higher order linear dynamical systems for smoke detection in video surveillance applications[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(5):1143-1154.[DOI:10.1109/TCSVT.2016.2527340]
Luo S, Yan C W, Wu K L, et al. Smoke detection based on condensed image[J]. Fire Safety Journal, 2015, 75:23-35.[DOI:10.1016/j.firesaf.2015.04.002]
Zhao Y Q, Li Q J, Gu Z. Early smoke detection of forest fire video using CS Adaboost algorithm[J]. Optik-International Journal for Light and Electron Optics, 2015, 126(19):2121-2124.[DOI:10.1016/j.ijleo.2015.05.082]
Li Y H, Shan L. Color detection algorithm of fire smoke based on video sequence[J]. Semiconductor Optoelectronics, 2016, 37(2):298-302.
黎粤华, 单磊.基于视频序列的火灾烟雾颜色检测算法[J].半导体光电, 2016, 37(2):298-302.[DOI:10.16818/j.issn1001-5868.2016.02.033]
Zhao Y Q, Zhou Z, Xu M M. Forest fire smoke video detection using spatiotemporal and dynamic texture features[J]. Journal of Electrical and Computer Engineering, 2015, 2015:40.[DOI:10.1155/2015/706187]
Chen H P, Liu F, Xu L, et al. Fire smoke detection in train carriages based on multiple features fusion[J]. Journal of Beijing Jiaotong University, 2015, 39(1):67-71, 77.
陈海鹏, 刘飞, 徐磊, 等.基于视频多特征融合的列车车厢烟雾检测方法[J].北京交通大学学报, 2015, 39(1):67-71, 77.[DOI:10.11860/j.issn.1673-0291-2015.01.011]
Sun J K, Yang R Y. Smoke detection in video based on color histogram and wavelets[J]. Computer Science, 2014, 41(12):251-254, 287.
孙建坤, 杨若瑜.基于颜色直方图和小波变换的视频烟雾检测[J].计算机科学, 2014, 41(12):251-254, 287.[DOI:10.11896/j.issn.1002-137X.2014.12.054]
Vieira D A G, Santos A L, Yehia H C, et al. Smoke detection in environmental regions by means of computer vision[C]//Proceedings of the 4th International Workshop, CIMA 2014. Cham: Springer, 2016: 135-151. [ DOI: 10.1007/978-3-319-26860-6_8 http://dx.doi.org/10.1007/978-3-319-26860-6_8 ]
Zheng H B, Zhai J Y. Forest fire smoke detection based on video analysis[J]. Journal of Nanjing University of Science and Technology, 2015, 39(6):686-691, 710.
郑怀兵, 翟济云.基于视频分析的森林火灾烟雾检测方法[J].南京理工大学学报, 2015, 39(6):686-691, 710.[DOI:10.14177/j.cnki.32-1397n.2015.39.06.009]
Kim H, Ryu D, Park J. Smoke detection using gmm and adaboost[J]. International Journal of Computer and Communication Engineering, 2014, 3(2):123-126.[DOI:10.7763/IJCCE.2014.V3.305]
Wang L, Li A G, Wang X N, et al. An early fire smoke detection method based on multi-features fusion[J]. Journal of Dalian Maritime University, 2014, 40(1):97-100.
王琳, 李爱国, 王新年, 等.基于多特征融合的早期火灾烟雾检测[J].大连海事大学学报, 2014, 40(1):97-100.[DOI:10.3969/j.issn.1006-7736.2014.01.025]
He D C, Lou X P, Tang H. Dynamic features based real-time video smoke detection[J]. Computer Applications and Software, 2014, 31(2):201-204.
何大超, 娄小平, 唐辉.基于动态特性的实时视频烟雾检测[J].计算机应用与软件, 2014, 31(2):201-204.[DOI:10.3969/j.issn.1000-386x.2014.02.054]
Wu D M, Li B P, Shen Y, et al. Smoke detection based on multi-feature fusion[J]. Journal of Graphics, 2015, 36(4):587-592.
吴冬梅, 李白萍, 沈燕, 等.基于多特征融合的烟雾检测[J].图学学报, 2015, 36(4):587-592.[DOI:10.3969/j.issn.2095-302X.2015.04.016]
Zhou B L, Song Y L, Yu M H. Fire smoke detection algorithm based on image disposal[J]. Fire Science and Technology, 2016, 35(3):390-393.
周泊龙, 宋英磊, 俞孟蕻.基于图像处理的火灾烟雾检测算法研究[J].消防科学与技术, 2016, 35(3):390-393.[DOI:10.3969/j.issn.1009-0029.2016.03.027]
Dong L F, Yu J K. Smoke detection method in video based on image separation[J]. Computer Engineering, 2015, 41(9):251-254, 260.
董兰芳, 余家奎.基于图像分离的视频烟雾检测方法[J].计算机工程, 2015, 41(9):251-254, 260.[DOI:10.3969/j.issn.1000-3428.2015.09.046]
Wang M L, Chen H P, Liu X. Research on smoke detection algorithm using dark channel prior[J]. Computer Applications and Software, 2014, 31(11):225-228, 232.
王孟柳, 陈和平, 刘夏.基于暗通道优先烟雾检测算法的研究[J].计算机应用与软件, 2014, 31(11):225-228, 232.[DOI:10.3969/j.issn.1000-386x.2014.11.057]
Ding H D, Liu S Y, Xu Y K, et al. Novel video smoke observation and detection method based on the motion track block[J]. Journal of Safety and Environment, 2016, 16(4):96-100.
丁怀对, 刘申友, 许玉坤, 等.基于运动块追踪的视频烟雾探测方法[J].安全与环境学报, 2016, 16(4):96-100.[DOI:10.13637/j.issn.1009-6094.2016.04.019]
Zhao L, Luo Y M, Luo X Y. Based on dynamic background update and dark channel prior of fire smoke detection algorithm[J]. Application Research of Computers, 2017, 34(3):957-960.
赵亮, 骆炎民, 骆翔宇.一种基于背景动态更新与暗通道先验的火灾烟雾检测算法[J].计算机应用研究, 2017, 34(3):957-960.[DOI:10.3969/j.issn.1001-3695.2017.03.074]
Jiao B L, Dong X. An early forest-fire smoke detection method based on non-parametric feature extraction[J]. Optical Technique, 2016, 42(1):20-23.
焦斌亮, 董雪.基于非参数特征提取的早期林火烟雾检测[J].光学技术, 2016, 42(1):20-23.[DOI:10.13741/j.cnki.11-1879/o4.2016.01.005]
Liu J L, Zho H W, Zhao T J. Research of flame detection on visual saliency method[J]. Journal of Computers, 2013, 8(12):3264-3271.
Shi Y K, Zhong Z, Zhang D X, et al. A study on smoke detection based on multi-feature[J]. Journal of Signal Processing, 2015, 31(10):1336-1341.
史玉坤, 仲贞, 张德馨, 等.利用多特征判别的烟雾检测方法研究[J].信号处理, 2015, 31(10):1336-1341.[DOI:10.3969/j.issn.1003-0530.2015.10.018]
Wang S D, He Y P, Zou J J, et al. Early smoke detection in video using swaying and diffusion feature[J]. Journal of Intelligent&Fuzzy Systems, 2014, 26(1):267-275.[DOI:10.3233/IFS-120735]
Prema C E, Vinsley S S, Suresh S. Multi feature analysis of smoke in YUV color space for early forest fire detection[J]. Fire Technology, 2016, 52(5):1319-1342.[DOI:10.1007/s10694-016-0580-8]
Zhou Z Q, Shi Y S, Gao Z F, et al. Wildfire smoke detection based on local extremal region segmentation and surveillance[J]. Fire Safety Journal, 2016, 85:50-58.[DOI:10.1016/j.firesaf.2016.08.004]
Tian H D, Li W Q, Wang L, et al. Smoke detection in video:an image separation approach[J]. International Journal of Computer Vision, 2014, 106(2):192-209.[DOI:10.1007/s11263-013-0656-6]
He H Q, Peng L Q, Yang D S, et al. Smoke detection based on a semi-supervised clustering model[C]//Proceedings of the 20th Anniversary International Conference on MultiMedia Modeling. Dublin, Ireland: Springer, 2014: 291-298. [ DOI: 10.1007/978-3-319-04117-9_27 http://dx.doi.org/10.1007/978-3-319-04117-9_27 ]
Pang C F, Yang S S, Chen N. Fire smoke recognition based on image entropy[J]. Journal of Jiangsu University of Science and Technology:Natural Science Edition, 2015, 29(1):52-57.
潘朝峰, 杨树森, 陈宁.基于图像熵的火灾烟雾识别[J].江苏科技大学学报:自然科学版, 2015, 29(1):52-57.[DOI:10.3969/j.issn.1673-4807.2015.01.009]
Ye W, Zhao J H, Wang S, et al. Dynamic texture based smoke detection using Surfacelet transform and HMT model[J]. Fire Safety Journal, 2015, 73:91-101.[DOI:10.1016/j.firesaf.2015.03.001]
Ye W, Zhao J H, Zhao Y, et al. Smoke detection based on surfacelet transform and dynamic texture[J]. Computer Engineering, 2015, 41(2):203-208.
叶威, 赵俭辉, 赵洋, 等.基于Surfacelet变换和动态纹理的烟雾检测[J].计算机工程, 2015, 41(2):203-208.[DOI:10.3969/j.issn.1000-3428.2015.02.039]
Li H D, Yuan F N. Image based smoke detection using pyramid texture and edge features[J]. Journal of Image and Graphics, 2015, 20(6):772-780.
李红娣, 袁非牛.采用金字塔纹理和边缘特征的图像烟雾检测[J].中国图象图形学报, 2015, 20(6):772-780.[DOI:10.11834/jig.20150606]
Favorskaya M, Pyataeva A, Popov A. Verification of smoke detection in video sequences based on spatio-temporal Local Binary Patterns[J]. Procedia Computer Science, 2015, 60:671-680.[DOI:10.1016/j.procs.2015.08.205]
Yuan F N, Fang Z J, Wu S Q, et al. Real-time image smoke detection using staircase searching-based dual threshold AdaBoost and dynamic analysis[J]. IET Image Processing, 2015, 9(10):849-856.[DOI:10.1049/iet-ipr.2014.1032]
Yuan F N, Shi J T, Xia X, et al. Sub oriented histograms of Local Binary Patterns for smoke detection and texture classification[J]. Ksii Transactions on Internet and Information Systems, 2016, 10(4):1807-1823.[DOI:10.3837/tiis.2016.04.019]
Yuan F N, Shi J T, Xia X, et al. High-order local ternary patterns with locality preserving projection for smoke detection and image classification[J]. Information Sciences, 2016, 372:225-240.[DOI:10.1016/j.ins.2016.08.040]
Yuan F N, Xia X, Shi J T, et al. Non-linear dimensionality reduction and gaussian process based classification method for smoke detection[J]. IEEE Access, 2017, 5:6833-6841.[DOI:10.1109/ACCESS.2017.2697408]
Turkey Bilkent University. Computer vision based fire detection software[EB/OL]. [2017-12-26] . http://signal.ee.bilkent.edu.tr/VisiFire/index.html http://signal.ee.bilkent.edu.tr/VisiFire/index.html .
土耳其比尔肯大学. 基于计算机视觉的火灾检测软件[EB/OL]. [2017-12-26] . http://signal.ee.bilkent.edu.tr/VisiFire/index.html http://signal.ee.bilkent.edu.tr/VisiFire/index.html .
Frizzi S, Kaabi R, Bouchouicha M, et al. Convolutional neural network for video fire and smoke detection[C]//Proceedings of IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society. Florence, Italy: IEEE, 2016: 877-882. [ DOI: 10.1109/IECON.2016.7793196 http://dx.doi.org/10.1109/IECON.2016.7793196 ]
Yin Z J, Wan B Y, Yuan F N, et al. A deep normalization and convolutional neural network for image smoke detection[J]. IEEE Access, 2017, 5:18429-18438.[DOI:10.1109/ACCESS.2017.2747399]
Keimyung University, Korea. Computer Vision and Pattern Recognition Laboratory[EB/OL]. [2017-12-26] . http://cvpr.kmu.ac.kr/ http://cvpr.kmu.ac.kr/ .
韩国启明大学. 计算机视觉与模式识别实验室[EB/OL]. [2017-12-26] . http://cvpr.kmu.ac.kr/ http://cvpr.kmu.ac.kr/ .
University of Science and Technology of China. State Key Laboratory of Fire Science[EB/OL]. [2017-12-26] . http://sklfs.ustc.edu.cn/ http://sklfs.ustc.edu.cn/ .
中国科学技术大学. 国家火灾科学重点实验室[EB/OL]. [2017-12-26] . http://sklfs.ustc.edu.cn/ http://sklfs.ustc.edu.cn/ .
Yuan Fei Niu. Video smoke detection of Jiangxi University of Finance and Economics. EB/OL]. [2017-12-26] . http://staff.ustc.edu.cn/~yfn/vsd.html http://staff.ustc.edu.cn/~yfn/vsd.html .
袁非牛. 视频烟雾检测[EB/OL]. [2017-12-26] . http://staff.ustc.edu.cn/~yfn/vsd.html http://staff.ustc.edu.cn/~yfn/vsd.html .
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