Online Adaptive Decision Fusion Framework Based on Entropic Projections onto Convex Sets with Application to Wildfire Detection in Video

TL;DR

This paper introduces an online adaptive decision fusion framework using entropic projections onto convex sets, applied to wildfire detection in video, demonstrating improved sequential data handling and decision accuracy.

Contribution

It presents a novel entropy-based online fusion method that adaptively combines multiple sub-algorithms with real-time weight updates, applicable to image analysis and video surveillance.

Findings

Effective wildfire detection in video sequences.

Successful application to standard datasets.

Real-time decision updating demonstrated.

Abstract

In this paper, an Entropy functional based online Adaptive Decision Fusion (EADF) 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 yielding 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 which are updated online according to an active fusion method based on performing entropic 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. The EADF framework is also tested with a standard dataset.