Cluster Computing

, Volume 22, Supplement 3, pp 7665–7675 | Cite as

The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier

  • Yuantao ChenEmail author
  • Weihong Xu
  • Jingwen Zuo
  • Kai Yang


For existed problems on fire detection fields, the traditional recognition methods on fire usually based on sensor’s signals are easily affected by the external environment elements. Meanwhile, most of the current methods based on feature extraction of fire image are less discriminative to different scene and fire type, and have lower recognition precision if the fire scene and type change. To overcome the drawback on fire recognition, the new fast recognition method for fire image has proposed by introducing color space information into Scale Invariant Feature Transform (SIFT) algorithm. Firstly, the feature descriptors of fire are extracted by SIFT algorithm from the fire images which are obtained from internet databases. Secondly, the local noisy feature points are filtered by introducing the feature information of fire color space. Thirdly, the feature descriptors are transformed into feature vectors, and then Incremental Vector Support Vector Machine classifier is utilized to establish the fast fire recognition model. The experiments are conducted on real-life fire image from internet. The experimental results had shown that for different fire scenes and types, the proposed algorithm has outperformed Kim’s method, Dimitropoulos’s method and Sumei’s method in terms of recognition accuracy and algorithm’s running speed. The proposed algorithm has better application prospects than Kim’s method, Dimitropoulos’s method and Sumei’s method.


Fire recognition Feature extraction SIFT feature Incremental vector support vector machine IV-SVM classifier 



This work is supported by the National Natural Science Foundation of China (No. 61702052), the Science and Technology Service Platform of Hunan Province (No. 2012TP1001), the Open Research Fund of Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation (No. 2015TP1005), the Changsha Science and Technology Planning (Nos. KQ1703018, KQ1706064), the Research Foundation of Education Bureau of Hunan Province (No. 12C0010, No. 17A007), the ZOOMLION Intelligent Technology Limited Company (No. 2017zkhx130), the Hunan Province Undergraduates Innovating Experimentation Project (No. (2016) 283-946), the Teaching and Reforming Project of Changsha University of Science and Technology (No. JG1755). We are grateful to anonymous referees for useful comments and suggestions.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yuantao Chen
    • 1
    Email author
  • Weihong Xu
    • 1
  • Jingwen Zuo
    • 2
  • Kai Yang
    • 3
  1. 1.Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation & School of Computer and Communicational EngineeringChangsha University of Science and TechnologyChangshaPeople’s Republic of China
  2. 2.Computer CenterCollege of ChengNan, Changsha University of Science and TechnologyChangshaPeople’s Republic of China
  3. 3.Zoomlion Intelligent Technology Company LimitedChangshaPeople’s Republic of China

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