基于Q-粗集的视频火灾威胁检测
Fire Threat Detection From Videos with Q-Rough Sets
摘要：
本文提出了一种新的无监督火灾区域分割和火灾威胁检测方法。火在控制中为人类文明服务于许多目的，但一旦它的蔓延变得不受控制，它同时也可能成为一种威胁。火灾区域分割和火灾非火灾分类方法很多。但是，目前确定火灾威胁的方法比较少，还没有一种无监督的方法。在这里，我们专注于开发一种无监督的方法，可以量化火灾的威胁，并相应地在室内和室外的自动监控系统中生成警报。在制定这种方法时，没有任何人工干预/标记数据集的火灾区域分割是一个主要挑战。在这里，我们使用粗略的近似近似火灾区域，并管理知识库的不完整性，由于缺乏任何先验信息。Q学习的效用最大化已被用于最小化粗糙近似中的模糊性。本文提出的新的集合近似方法被称为Q-粗集。它用于从视频帧中分割火灾区域。输入视频流上的火灾火焰的威胁指数已经与最近帧上的火灾段的相对增长同步地被定义。本文定义的所有理论和指标都已通过不同类型的火灾视频进行了实验验证，通过演示和比较，优于现有技术。
英文摘要：
This article defines new methods for unsupervised fire region segmentation and fire threat detection from video stream. Fire in control serves a number of purposes to human civilization, but it could simultaneously be a threat once its spread becomes uncontrolled. There exists many methods on fire region segmentation and fire non-fire classification. But the approaches to determine the threat associated with fire is relatively scare, and no such unsupervised method has been formulated yet. Here we focus on developing an unsupervised method with which the threat of fire can be quantified and accordingly generate an alarm in automated surveillance systems in indoor as well as in outdoors. Fire region segmentation without any manual intervention/ labelled data set is a major challenge while formulating such a method. Here we have used rough approximations to approximate the fire region, and to manage the incompleteness of the knowledge base, due to absence of any prior information. Utility maximization of Q-learning has been used to minimize ambiguities in the rough approximations. The new set approximation method, thus developed here, is named as Q-rough set. It is used for fire region segmentation from video frames. The threat index of fire flame over the input video stream has been defined in sync with the relative growth in the fire segments on the recent frames. All theories and indices defined here have been experimentally validated with different types of fire videos, through demonstrations and comparisons, as superior to the state of the art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
