Autonomous Flame Detection in Videos with a Dirichlet Process Gaussian Mixture Color Model

Zhenglin Li, Lyudmila S. Mihaylova, Olga Isupova, Lucile Rossi

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

This paper proposes a flame detection framework based on the color, dynamics, and flickering properties of flames. The distribution of flame colors is modeled by a Gaussian mixture model whose number of Gaussian components is estimated by a Dirichlet process from training data rather than set empirically. The proposed approach estimates the flame color distribution more accurately as it can determine the number of Gaussian components of the mixture model automatically. Additionally, a probabilistic saliency analysis method and a one-dimensional wavelet transform are used to extract motion saliency and filtered temporal series as features, describing the dynamics and flickering properties of flames. The developed Dirichlet process Gaussian mixture model based approach for autonomous flame detection is tested on various videos and achieves framewise accuracy higher than 95%.

Original languageEnglish
Pages (from-to)1146-1154
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume14
Issue number3
DOIs
Publication statusPublished - 1 Nov 2017

Keywords

  • Dirichlet process Gaussian mixture model (DPGMM)
  • flame detection
  • saliency analysis

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