  Due to industry deployment and extension of urban areas, early warning
systems have an essential role in giving emergency. Fire is an event that can
rapidly spread and cause injury, death, and damage. Early detection of fire
could significantly reduce these injuries. Video-based fire detection is a low
cost and fast method in comparison with conventional fire detectors. Most
available fire detection methods have a high false-positive rate and low
accuracy. In this paper, we increase accuracy by using spatial and temporal
features. Captured video sequences are divided into Spatio-temporal blocks.
Then a saliency map and combination of color and texture features are used for
detecting fire regions. We use the HSV color model as a spatial feature and
LBP-TOP for temporal processing of fire texture. Fire detection tests on
publicly available datasets have shown the accuracy and robustness of the
algorithm.
