| Summary: | As part of the early warning system, forest fire detection has a critical role in detecting fire
in a forest area to prevent damage to forest ecosystems. In this case, the speed of the detection process
is the most critical factor to support a fast response by the authorities. Thus, this article proposes a
new framework for fire detection based on combining color-motion-shape features with machine
learning technology. The characteristics of the fire are not only red but also from their irregular shape
and movement that tends to be constant at specific locations. These characteristics are represented
by color probabilities in the segmentation stage, color histograms in the classification stage, and
image moments in the verification stage. A frame-based evaluation and an intersection over union
(IoU) ratio was applied to evaluate the proposed framework. Frame-based evaluation measures the
performance in detecting fires. In contrast, the IoU ratio measures the performance in localizing
the fires. The experiment found that the proposed framework produced 89.97% and 10.03% in the
true-positive rate and the false-negative rate, respectively, using the VisiFire dataset. Meanwhile, the
proposed method can obtain an average of 21.70 FPS in processing time. These results proved that
the proposed method is fast in the detection process and can maintain performance accuracy. Thus,
the proposed method is suitable and reliable for integrating into the early warning system.
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