@article{ref1,
title="BLSTM based night-time wildfire detection from video",
journal="PLoS one",
year="2022",
author="Agirman, Ahmet K. and Taşdemir, Kasım",
volume="17",
number="6",
pages="e0269161-e0269161",
abstract="Distinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. This makes the analysis of temporal behavior of night-time fire indispensable for classification. To this end, a BLSTM based night-time wildfire event detection from a video algorithm is proposed. It is shown in the experiments that the proposed algorithm attains 95.15% of accuracy when tested against a wide variety of actual recordings of night-time wildfire incidents and 23.7 ms per frame detection time. Moreover, to pave the way for more targeted solutions to this challenging problem, experiment-based thorough investigations of possible sources of incorrect predictions and discussion of the unique nature of night-time wildfire videos are presented in the paper.<p /> <p>Language: en</p>",
language="en",
issn="1932-6203",
doi="10.1371/journal.pone.0269161",
url="http://dx.doi.org/10.1371/journal.pone.0269161"
}