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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Wildfire detection using LMS based active learning

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      Author
      Töreyin, B. Uğur
      Çetin, A. Enis
      Date
      2009-04
      Source Title
      ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
      Publisher
      IEEE
      Pages
      1461 - 1464
      Language
      English
      Type
      Conference Paper
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      Abstract
      A computer vision based algorithm for wildfire detection is developed. The main detection algorithm is composed of four sub-algorithms detecting (i) slow moving objects, (ii) gray regions, (iii) rising regions, and (iv) shadows. Each algorithm yields its own decision as a real number in the range [-1,1] at every image frame of a video sequence. Decisions from subalgorithms are fused using an adaptive algorithm. In contrast to standard Weighted Majority Algorithm (WMA), weights are updated using the Least Mean Square (LMS) method in the training (learning) stage. The error function is defined as the difference between the overall decision of the main algorithm and the decision of an oracle, who is the security guard of the forest look-out tower. ©2009 IEEE.
      Keywords
      Active learning
      Least mean square methods
      Wildfire detection
      Detection algorithm
      Error function
      Gray region
      Image frames
      Least mean square method
      Real number
      Security guards
      Slow moving objects
      Video sequences
      Weighted majority algorithm
      Acoustics
      Adaptive algorithms
      Computer vision
      Education
      Fires
      Number theory
      Signal detection
      Signal processing
      Video recording
      Learning algorithms
      Permalink
      http://hdl.handle.net/11693/26735
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/ICASSP.2009.4959870
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      • Department of Electrical and Electronics Engineering 3524
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