An Efficient Fire Detection Method based on Orientation Feature

This paper proposes a novel method for reliable fire detection. The burning fire usually causes rich moving features in terms of directions, which can offer the best chance to distinguish between the fire region and the non-fire one. Motivated by this observation, we design a novel orientation feature to represent this characteristic. Based on this feature, a method is proposed to detect the fire efficiently. First, fire color is utilized to extract the fire candidate areas from the surveillance video. Then, the direction is obtained by computing the optical flow for each pixel in the candidate area. The directions are discretized to four parts. By counting the percentage of pixels whose moving directions fall into these four parts in a period of time, and combining with the two parameters, i.e., both of the number of frames without the moving directions and the number of consecutive frames in the candidate area, we use these six parameters as the fire orientation feature. In the end, by training a support vector machine (SVM) classifier with the input of our fire orientation feature, the candidate area is judged whether it is a fire. Our main contribution is that we design the novel fire orientation feature. The feature can not only characterize the fire intrinsic dynamic properties accurately but also is very efficient. Compared with the art-of-state methods, the experimental results confirm that our approach significantly improves the accuracy of fire detection and impressively decreases the false alarm rate. The detection speed of our approach is also very competitive with the art-of-state fire detection methods.