Flame detection in video using hidden Markov models

TLDR

The paper proposes a novel flame detection method that processes ordinary camera video to identify fire. The method employs hidden Markov models to capture flame flicker and spatial color variations, distinguishing flame from flame‑colored moving objects, and integrates these cues for a final detection decision. The approach significantly reduces false alarms from flame‑colored moving objects compared to existing video‑based fire detection systems.

Abstract

This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.