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Visual Smoke Detection

  • Abhishek Kumar TripathiEmail author
  • Shanti Swarup
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

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.

Keywords

False Alarm Optical Flow Gaussian Mixture Model Fuzzy Membership Hurst Exponent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Image Understanding GroupUurmi Systems Pvt. Ltd.HyderabadIndia

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