BLSTM based night-time wildfire detection from video
Özet
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.
