  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.
