[1]WU Xiyin,YAN Yunyang,DU Jing,et al.Fire detection based on fusion of multiple features[J].CAAI Transactions on Intelligent Systems,2015,10(2):240-247.[doi:10.3969/j.issn.1673-4785.201406022]
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Fire detection based on fusion of multiple features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 2 Page number: 240-247 Column: 学术论文—机器感知与模式识别 Public date: 2015-04-25
Title:
Fire detection based on fusion of multiple features
Author(s):
WU Xiyin1; 2;  YAN Yunyang1; 2;  DU Jing1; 2;  GAO Shangbing2;  LIU Yi’an1
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Faculty of Computer Engineering, Huaiyin Institute of Technology, Huaian 223003, China
Keywords:
feature extraction; feature fusion; support vector machine; color model; fire detection; circularity measures; rectangularity; orthocenter height
CLC:
TP391.41
DOI:
10.3969/j.issn.1673-4785.201406022
Abstract:
Video fire detection is an important method to prevent fire disaster under complex circumstances. In order to improve the efficiency and robustness of fire detection, the color feature model can be improved based on RGB and HSI color space and the suspected flame area is extracted effectively. After analysis on the experimental results with different features of shape or structure of fire and the influence of their combined features on the validity of fire detection, a method of flame detection is proposed based on fusion of circularity, rectangularity and the coefficient of orthocenter height. Based on fusion of these flame features, the support vector machine (SVM) is used for classification. Experimental results on the fire videos at Bilkent University show that the proposed algorithm is efficient and fast for fire detection, and it could detect fire real-time under a variety of circumstances.
References:
[1] SASIREKHA M S P, RAMYA M S, PRASANTH M R M, et al. A survey about automatic flame/fire detection in videos[J]. International Journal of Research in Advent Technology, 2014, 2(2): 145-150.
[2] ZHANG L, LIU X. Fire recognition based on multiple features of video images[C]//Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering. Paris, France, 2013: 1597-1600.
[3] 严云洋, 唐岩岩, 郭志波, 等. 融合色彩和轮廓特征的火焰检测[J]. 微电子学与计算机, 2011, 10: 137-141, 145.YAN Yunyang, TANG Yanyan, GUO Zhibo, et al. Fusion of flame color and its contour for fire detection[J]. Microelectronics & Computer, 2011, 10: 137-141, 145.
[4] CHEN L H, HUANG W C. Fire detection using spatial-temporal analysis[C]//Proceedings of the World Congress on Engineering. London, UK, 2013: 2222-2225.
[5] CHEN T H, WU P H, CHIOU Y C. An early fire-detection method based on image processing[C]//International Conference on Image Processing (ICIP).[S.l], China, 2004: 1707-1710.
[6] HABIBOGLU Y, GUNAY O, CETIN A E. Flame detection method in video using covariance descriptors[C]//IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP). Prague, Czech, 2011: 1817-1820.
[7] 王莹, 李文辉. 基于多特征融合的高精度视频火焰检测算法[J]. 吉林大学学报: 工学版, 2010(3): 769-775.WANG Ying, LI Wenhui. High-precision video flame detection algorithm based on multi-feature fusion[J]. Journal of Jilin University: Engineering and Technology Edition, 2010(3): 769-775.
[8] MENGXIN L I, WEJING X U, KE X U, et al. A new hybrid feature extraction method based on accurate motion area[J]. Journal of Electrical Engineering, 2013, 11(10): 5563-5570.
[9] CHEN J, HE Y, WANG J. Multi-feature fusion based fast video flame detection[J]. Building and Environment, 2010, 45(5): 1113-1122.
[10] 严云洋, 唐岩岩, 刘以安, 等. 使用多尺度LBP特征和SVM的火焰识别算法[J]. 山东大学学报: 工学版, 2012(5): 47-52, 58.YAN Yunyang, TANG Yanyan, LIU Yi’an, et al. Flame detection based on LBP features with multi-scales and SVM[J]. Journal of Shandong University: Engineering Science, 2012(5): 47-52, 58.
[11] LEI W, LIU J. Early fire detection in coalmine based on video processing[C]//International Conference on Communication, Electronics and Automation Engineering. Berlin, German, 2013: 239-245.
[12] YANG X, WANG J, HE S. A SVM approach for vessel fire detection based on image processing[C]//Proceedings of International Conference on Modelling, Identification & Control (ICMIC). Wuhan, China, 2012: 150-153.
[13] XI Z, FANG X, ZHEN S, et al. Video flame detection algorithm based on multi-feature fusion technique[C]// Control and Decision Conference (CCDC). Guiyang, China, 2012: 4291-4294.
[14] ZHAO J, ZHANG Z, HAN S, et al. SVM based forest fire detection using static and dynamic features[J]. Computer Science and Information Systems, 2011, 8(3):821-841.
[15] 闵永林. 大空间智能消防水炮灭火系统研究[D]. 上海: 上海大学, 2010: 58-73.MIN Yonglin. Intelligent water fire monitor systems applied in large space places[D]. Shanghai: Shanghai University, 2010: 58-73.
[16] XUAN T T, 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.
[17] OJALA T, PIETILAINEN M, MAENPAA T. Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns[J]. Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
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