Robust fire detection using logistic regression and randomness testing for real-time video surveillance

This paper proposes a real-time fire detection algorithm for video surveillance. Firstly, candidate fire regions (CFRs) are detected using modified conventional methods, that is, the detection of moving regions and fire-colored pixels. In order to avoid false alarms, effective color and shape-based features are extracted from CFRs. Then, the set of features are fed into the logistic regression to classify the fire and non-fire regions. A randomness test over the features is further adopted for the final fire verification. Experimental results show that the proposed approach is more robust and fast. False alarms due to ordinary motion of flame colored moving objects are reduced with a great amount, compared to the existing video based fire detection systems.