[1]吴茜茵,严云洋,杜静,等.多特征融合的火焰检测算法[J].智能系统学报,2015,10(2):240-247.[doi:10.3969/j.issn.1673-4785.201406022]
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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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]
多特征融合的火焰检测算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
10
期数:
2015年第2期
页码:
240-247
栏目:
学术论文—机器感知与模式识别
出版日期:
2015-04-25
- Title:
- Fire detection based on fusion of multiple features
- Keywords:
- feature extraction; feature fusion; support vector machine; color model; fire detection; circularity measures; rectangularity; orthocenter height
- 分类号:
- TP391.41
- 文献标志码:
- A
- 摘要:
- 视频火焰检测是复杂场景下预防火灾的重要方法。为了提高火焰的检测效率和鲁棒性,基于RGB和HSI颜色空间改进了火焰的颜色特征模型,有效地提取了疑似火焰区域;实验对比分析了火焰不同的形状结构特征,及其特征组合对火焰检测有效性的影响,提出了一种融合圆形度、矩形度和重心高度系数的火焰检测算法,然后将融合后的火焰特征输入支持向量机(SVM)中进行分类。在Bilkent大学火灾视频库上的实验结果表明,该方法高效、快速,且能适用于多种场景。
- 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.
- 参考文献/References:
-
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备注/Memo
收稿日期:2014-6-13;改回日期:。
基金项目:教育部科学技术研究重大资助项目(311024);国家自然科学基金资助项目(61402192);淮安市科技计划资助项目(HAG2013057,HAG2013059).
作者简介:吴茜茵,女,1990年生,硕士研究生,CCF会员,主要研究方向为数字图像处理、模式识别;严云洋,男,1967年生,教授,博士,江苏省计算机学会常务理事及人工智能专委会副主任委员,主要研究方向为数字图像处理、模式识别,发表学术论文80余篇,其中被SCI、EI收录40余篇;杜静,女,1988年生,硕士研究生,主要研究方向为数字图像处理、模式识别。
通讯作者:严云洋.E-mail:areyyyke@163.com.
更新日期/Last Update:
2015-06-15