Real-time detection of fire flame in video scenes from a surveillance camera offers early warning to
ensure prompt reaction to devastating fire hazards. Many existing fire detection methods based on
computer vision technology have achieved high detection rates, but often with unacceptably high falsealarm
rates. This paper presents a reliable visual analysis technique for fast fire flame detection in surveillance
video using logistic regression and temporal smoothing. A candidate fire region is determined
according to the color component ratio and motion cue of fire flame obtained by background subtraction.
The candidate fire region is examined for genuine fire flame in terms of the proposed fire probability
computed using logistic regression of prominent features of size, motion, and color information. Temporal
smoothing is employed to reduce false alarm rates at a slight decrease in sensitivity. Experiments
conducted on various benchmarking databases demonstrate that the proposed scheme successfully
distinguishes fire flame from the background as well as moving fire-like objects in real-world indoor and
outdoor video surveillance settings. The average time to fire detection was fastest among the state-ofthe-art
video-based fire flame detection techniques for comparison.