FMF的火焰显著性检测

doi:10.16265/j.cnki.issn1003-3033.2019.05.010

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (5): 56-61.doi: 10.16265/j.cnki.issn1003-3033.2019.05.010

FMF的火焰显著性检测

李云, 张晴副教授, 沈子豪, 左保川

上海应用技术大学计算机科学与信息工程学院,上海 201418

收稿日期:2019-01-05 修回日期:2019-02-25 发布日期:2020-11-02
作者简介:李云(1994—),男,江苏苏州人,硕士研究生,主要研究方向为安全工程、计算机视觉。E-mail:370793890@qq.com。
基金资助:
国家自然科学基金资助(61401281, 61806126, 41671402);上海应用技术大学中青年教师科技人才发展基金资助(ZQ2018-23)。

Flame saliency detection based on FMF

LI Yun, ZHANG Qing, SHEN Zihao, ZUO Baochuan

School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China

Received:2019-01-05 Revised:2019-02-25 Published:2020-11-02

摘要/Abstract

摘要： 为准确定位火源点,实现火灾预警,提出一种基于人眼视觉注意机制的实时监测火灾预警方法。首先,根据图像对抗理论,提取视频序列中每一帧图像的亮度和颜色特征;其次,运用像素级显著性检测算法,构建描述特征信息的多尺度空间高斯金字塔;然后,运用跨尺度特征相加方法,融合中心-邻域对比度金字塔,得到静态显著性图;最后,结合动态帧差法,将多特征融合(FMF)算法得到的显著性图作动态帧差,寻找视频帧中属于火焰的区域,在公开的数据集上就4种评价指标与6种代表性算法作对比。结果表明:FMF算法通过显著性分析方法描述多尺度空间特征信息,其鲁棒性更强;与6种算法相比,FMF算法在准确率和漏检率上有较明显的优势,且能准确识别与定位火焰,防范火灾的发生。

关键词: 火焰检测, 对抗理论, 显著性检测, 多特征融合(FMF), 动态帧差法

Abstract: To accurately locate the fire source and achieve early warning of fire, a real-time fire warning monitoring method based on human visual attention mechanism was developed. Firstly, the brightness and color features of each frame of the video sequence were extracted according to image opponent theory. Secondly, pixel-level saliency detection algorithm was applied to construct a multi-scale spatial Gaussian pyramid which describes feature information. Then, the static saliency map was generated by merging center-neighbor contrast pyramid through the cross-scale feature addition method. Finally, the saliency map obtained from FMF algorithm was used as the dynamic frame difference based on the dynamic frame difference method to find the region of flame on video frames, and the proposed approach was compared with 6 representative algorithms in terms of 4 performance criteria on public datasets. The results show that FMF algorithm demonstrates stronger robustness in describing multi-scale spatial feature information through the saliency analysis method, and with obvious advantages in accuracy and missed rate compared with other algorithms, it can accurately identify and locate the flame so as to prevent the occurrence of fire accidents.

Key words: flame detection, opponent theory, saliency detection, fusion of multi-feature (FMF), dynamic frame difference method

中图分类号:

X932

李云, 张晴, 沈子豪, 左保川. FMF的火焰显著性检测[J]. 中国安全科学学报, 2019, 29(5): 56-61.

LI Yun, ZHANG Qing, SHEN Zihao, ZUO Baochuan. Flame saliency detection based on FMF[J]. China Safety Science Journal, 2019, 29(5): 56-61.

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FMF的火焰显著性检测

Flame saliency detection based on FMF

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