Hybrid fire detection using hidden Markov model and luminance map

  • Authors:

  • Liqiang Wang;Mao Ye;Jian Ding;Yuanxiang Zhu

  • Affiliations:

  • School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China and State Key Lab for Novel Software Technology, Nanjing University, ...;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China and State Key Lab for Novel Software Technology, Nanjing University, ...;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China and State Key Lab for Novel Software Technology, Nanjing University, ...;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China and State Key Lab for Novel Software Technology, Nanjing University, ...

  • Venue:
  • Computers and Electrical Engineering
  • Year:
  • 2011

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Abstract

Recently, fire detection is a hot research topic. Although many detection methods have been proposed, there exist high false alarms because of the interference of fire-colored moving object in the complex environments. In this paper, a hybrid method is proposed. First, we get the set of candidate fire regions. Then these candidate fire regions are analyzed to exclude the fire-colored moving object. Our contributions are using the hidden Markov model (HMM) based on spatio-temporal feature and the variance of luminance map motivated by visual attention, and combining both for fire detection. The wrong detection can be reduced greatly. Experiment results show our proposed method has a good performance and it is robust to be used in complex environment compared with previous algorithms.