Real time video fire detection using spatio-temporal consistency energy
In this paper a new algorithm is proposed for detecting fire from video data in real time based on a combination of features, including a new spatio-temporal consistency feature. A significant challenge in flame detection systems is to discriminate between actual fire and false alarms caused by fire colored objects. Towards this aim, we propose an algorithm that has significant advantages: a) it is fast since rectangular non-overlapping blocks are used as basic elements (instead of arbitrary-shaped regions) b) a spatio-temporal consistency feature is used in addition to color probability, spatial energy, flickering and spatio-temporal energy features and c) an improved rule-based classification approach is proposed, after evaluating seven different classification approaches. Experimental results are presented that confirm the efficiency of the proposed approach.