Research on video flame detection algorithm based on improved DS evidence theory
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Research on video flame detection algorithm based on improved DS evidence theory Yueyan Qin 1 & Jiangtao Cao 1 & Xiaofei Ji 2
& Yu Zhang
1
Received: 14 August 2019 / Revised: 10 June 2020 / Accepted: 29 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Flame detection . complementary features . DS evidence theory . multi-classifier fusion
* Xiaofei Ji [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction The occurrence of fires has caused huge threats and losses to people’s lives, natural resources, and social property, etc.. Therefore, researchers have been working hard for decades to develop reliable fire detection technology. With the popularization of video surveillance technology in recent years, video based fire detection technology has gained more and more attention. Compared with sensor fire detection, video fire detection has the advantages of large detection range, quick response and convenient confirmation. However, the real fire scenes are generally more complex and changeable, or there e
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