An Intelligent Automatic Early Detection System of Forest Fire Smoke Signatures using Gaussian Mixture Model


Seok-Hwan Yoon, Joonyoung Min, Journal of Information Processing Systems Vol. 9, No. 4, pp. 621-632, Dec. 2013  

10.3745/JIPS.2013.9.4.621
Keywords: Forest Fire Detection, Gaussian Mixture Models, HSL Color Space, Smoke Signature
Fulltext:

Abstract

The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.


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Cite this article
[APA Style]
Yoon, S. & Min, J. (2013). An Intelligent Automatic Early Detection System of Forest Fire Smoke Signatures using Gaussian Mixture Model. Journal of Information Processing Systems, 9(4), 621-632. DOI: 10.3745/JIPS.2013.9.4.621.

[IEEE Style]
S. Yoon and J. Min, "An Intelligent Automatic Early Detection System of Forest Fire Smoke Signatures using Gaussian Mixture Model," Journal of Information Processing Systems, vol. 9, no. 4, pp. 621-632, 2013. DOI: 10.3745/JIPS.2013.9.4.621.

[ACM Style]
Seok-Hwan Yoon and Joonyoung Min. 2013. An Intelligent Automatic Early Detection System of Forest Fire Smoke Signatures using Gaussian Mixture Model. Journal of Information Processing Systems, 9, 4, (2013), 621-632. DOI: 10.3745/JIPS.2013.9.4.621.