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http://dx.doi.org/10.13067/JKIECS.2012.7.4.733

Gaussian Mixture Model Based Smoke Detection Algorithm Robust to Lights Variations  

Park, Jang-Sik (경성대학교 전자공학과)
Song, Jong-Kwan (경성대학교 전자공학과)
Yoon, Byung-Woo (경성대학교 전자공학과)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.7, no.4, 2012 , pp. 733-739 More about this Journal
Abstract
In this paper, a smoke detection algorithm robust to brightness and color variations depending on time and weather is proposed. The proposed smoke detection algorithm specifies the candidate region using difference images of input and background images, determines smoke by comparing feature coefficients of Gaussian mixture model of difference images. Thresholds for specifying candidate region is divided by four levels according to average brightness and chrominance of input images. Clusters of Gaussian mixture models of difference images are aligned according to average brightness. Smoke is determined by comparing distance of Gaussian mixture model parameters. The proposed algorithm is implemented by media dedicated DSP. As results of experiments, it is shown that the proposed algorithm is effective to detect smoke with camera installed outdoor.
Keywords
Video; Smoke; Lights; Background estimation; Gaussian mixture model;
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