Flame detection in video using hidden Markov models

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.

Video fire detection – Review

“…Smoke at far distances (> 100 m to the camera) exhibits different spatio-temporal characteristics than nearby smoke and fire [71], [59], [72]. This demands specific methods explicitly developed for smoke detection at far distances rather than using nearby smoke detection meth- …”

Online Detection of Fire in Video

“…Earlier fire and flame detection algorithms are based on the use of color and motion information in video [10]. There are also some recent papers proposing methods which characterize the specific motion of flames [12], [17], [5], [18], [20]. Other recent methods for video based fire detection are [9], [21], [19].…”

A Vision-Based Approach to Fire Detection

“…Checking the dynamic characteristics of the potential fire's outline is also good practice for the reduction of false positives [11,12]. In order to also take into account the typical fire's dynamic texture, spatio-temporal wavelet analysis can also be applied [3,13]. The idea is to exploit the well-known flickering and textured characteristics of flames [14] for their detection.…”