Research on video flame detection algorithm based on improved DS evidence theory

Abstract

The detection and prevention of flame is of great value to protect people’s lives and property safety. At present, most flame detection methods use a single classifier and have achieved some results. However, a single classification algorithm has poor adaptability to fire detection in a variety of complex situations. Therefore, a multi-classifier fusion flame detection algorithm is proposed based on Dempster-Shafer (DS) evidence theory is proposed. In short, firstly four classifiers are used to classify the same flame feature, and the four classification results are fused to make preliminary decision. The four classifiers include support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT) and random forest (RF). Second, three complementary flame features are chosen, namely color, texture and shape changes. Finally, the preliminary decision results of the three features are fused to obtain the final classification result. It should be noted that when different classifiers have strong conflicts on the classification result of the same feature, the fusion rule of DS evidence theory will be invalid. To solve this problem, the DS evidence theory is improved. For the experiment, the public flame videos are collected to construct a data set including different complex scenes for algorithm verification, where the frame rate of the video is 15 or 24 frames/s and the resolution is 320 × 240. The experimental results show that there is a strong complementary among the results of different single classifier. The multi-classifier fusion algorithm can achieve better classification performance and robust performance than the single classifier by integrating the results of each classifier, and its average detection rate reaches 93.08%. In addition, for the changes of different environments, the proposed method has higher adaptability and stability than other state-of-art methods.