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%.
|Number of pages||9|
|Journal||IEEE Transactions on Industrial Informatics|
|Publication status||Published - 1 Nov 2017|
- Dirichlet process Gaussian mixture model (DPGMM)
- flame detection
- saliency analysis
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- Department of Computer Science - Lecturer
- Artificial Intelligence
- EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
Person: Research & Teaching