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http://dx.doi.org/10.17661/jkiiect.2016.9.4.343

Realtime Smoke Detection using Hidden Markov Model and DWT  

Kim, Hyung-O (Department of Electrical Engineering, SeoIl University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.9, no.4, 2016 , pp. 343-350 More about this Journal
Abstract
In this paper, We proposed a realtime smoke detection using hidden markov model and DWT. The smoke type is not clear. The color of the smoke, form, spread direction, etc., are characterized by varying the environment. Therefore, smoke detection using specific information has a high error rate detection. Dynamic Object Detection was used a robust foreground extraction method to environmental changes. Smoke recognition is used to integrate the color, shape, DWT energy information of the detected object. The proposed method is a real-time processing by having the average processing speed of 30fps. The average detection time is about 7 seconds, it is possible to detect early rapid.
Keywords
Smoke Detection; Hidden Markov Model; DWT; Object Detection; GMM; foreground;
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