Töreyin, B. UğurÇetin, A. Enis2016-02-082016-02-082009-04http://hdl.handle.net/11693/26735Date of Conference: 19-24 April 2009Conference name: 2009 IEEE International Conference on Acoustics, Speech and Signal ProcessingA 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.EnglishActive learningLeast mean square methodsWildfire detectionDetection algorithmError functionGray regionImage framesLeast mean square methodReal numberSecurity guardsSlow moving objectsVideo sequencesWeighted majority algorithmAcousticsAdaptive algorithmsComputer visionEducationFiresNumber theorySignal detectionSignal processingVideo recordingLearning algorithmsConference Paper10.1109/ICASSP.2009.4959870