Video Flame Detection Method Based on Improved Fast Robust Feature

Flame detection has important practical significance. Based on the uniqueness of the color information of the flame, using the flame's color model to initially extract the suspected flame area to improve the accuracy of flame detection in complex environments. In order to reduce the amount of unnecessary algorithm calculation, an improved feature extraction method combing color information with acceleration robust combining features (SURF) is proposed. On this basis, the morphological features of flame, such as roundness and rectangularity, are added as auxiliary classification features, and the flame region features extracted from the image are input to support vector machine (SVM) for training and learning. The experimental results show that the flame detection method proposed in this paper is applicable to a wider range of scenes, with the advantages of high accuracy and robustness, high reliability, small calculation amount, short detection time, and still has good robustness in complex scenes with more interferences.