An effective algorithm to detect both smoke and flame using color and wavelet analysis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-01

AUTHORS

Shiping Ye, Zhican Bai, Huafeng Chen, R. Bohush, S. Ablameyko

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. More... »

PAGES

131-138

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s1054661817010138

DOI

http://dx.doi.org/10.1134/s1054661817010138

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084223305


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