A Flame Detection Method Based on Novel Gradient Features

Abstract

In this study, we present a novel approach to efficiently detect the flame in multiple scenes in an image. The method uses a set of parametric representation named as Gradient Features (GF), to learn the features of flame color changes in the image. Different from the traditional color features of the flame, GF represents the color changes in RGB channels for further consideration. In this study, support vector machine was applied to generate a set of candidate regions and the decision tree model was used to judge flame regions based on GF. Some exclusive experiments were conducted to verify the validity and effectiveness of the proposed method. The results showed that the proposed method can accurately differentiate between yellow color light and sunrise scenes. A comparison with the state-of-the-art preceding methods showed that this method can utilize the symmetry of flame regions and achieve a better result.

Keywords

• flame detection
• svm
• decision tree
• pca
• gradient features