Abstract
In this paper, we have proposed a novel and efficient visual smoke detection algorithm. Smoke detection in video surveillance is very important for early fire detection. Proposed algorithm uses an unique combination of features to detect smoke efficiently. These features use appearance, energy and motion properties of the smoke. Further analysis of past history of smoke increases the accuracy of the algorithm. These features are less complex and enable the algorithm for real time application. A general assumption is that smoke is a low frequency signal which may smoothen the background. We focused on the nature of the smoke (shape disorder, energy reduction and variability over time) and proposed a novel algorithm which requires no user intervention and prior data training. Due to the large variability in the feature values, we assigned the fuzzy membership to these features instead of hard thresholding to reduce classification errors. Simulation carried out with available dataset, show that smoke is accurately localized both in time and space via proposed approach.
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Tripathi, A.K., Swarup, S. (2017). Visual Smoke Detection. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_9
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DOI: https://doi.org/10.1007/978-3-319-54407-6_9
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