Please use this identifier to cite or link to this item:
|Title:||An Effective Algorithm to Detect Both Smoke and Flame Using Color and Wavelet Analysis|
Ablameyko, Sergey V.
|Keywords:||ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика|
|Publisher:||Pleiades Publishing, Ltd.|
|Citation:||Pattern Recognition and Image Analysis, 2017, Vol. 27, No. 1, pp. 131–138.|
|Abstract:||Fire detection is an important task in many applications. Smoke and flame are two essential symbols of fire in images. In this paper, we propose an algorithm to detect smoke and flame simultaneously for color dynamic video sequences obtained from a stationary camera in open space. Motion is a common feature of smoke and flame and usually has been used at the beginning for extraction from a current frame of candidate areas. The adaptive background subtraction has been utilized at a stage of moving detection. In addition, the optical flow-based movement estimation has been applied to identify a chaotic motion. With the spatial and temporal wavelet analysis, Weber contrast analysis and color segmentation, we achieved moving blobs classification. Real video surveillance sequences from publicly available datasets have been used for smoke detection with the utilization of our algorithm. We also have conducted a set of experiments. Experiments results have shown that our algorithm can achieve higher detection rate of 87% for smoke and 92% for flame|
|Appears in Collections:||2017|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.