Wildfire detection using LMS based active learning
Author
Töreyin, B. Uğur
Çetin, A. Enis
Date
2009-04Source Title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher
IEEE
Pages
1461 - 1464
Language
English
Type
Conference PaperItem Usage Stats
154
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189
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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 learningLeast 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