Online adaptive decision fusion framework based on projections onto convex sets with application to wildfire detection in video
Date
2011-07-06Source Title
Optical Engineering
Print ISSN
0091-3286
Publisher
S P I E - International Society for Optical Engineering
Volume
50
Issue
7
Pages
77202-1 - 77202-12
Language
English
Type
ArticleItem Usage Stats
134
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98
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Abstract
In this paper, an online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing orthogonal projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system is developed to evaluate the performance of the algorithm in handling the problems where data arrives sequentially. In this case, the oracle is the security guard of the forest lookout tower verifying the decision of the combined algorithm. Simulation results are presented.
Keywords
Active learningDecision fusion
Online learning
Projection onto convex sets
Wild-fire detection