Fire Detection Algorithms Using Multimodal ... - Bilkent University

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CHAPTER 6. WILDFIRE DETECTION 99Active Decision Fusion(x,n)for i = 1 to M dow i (0) = 1 , InitializationMend forŷ(x, n) = ∑ i w i(n)D i (x, n)if ŷ(x, n) ≥ 0 thenreturn 1elsereturn -1end ife(x, n) = y(x, n) − ŷ(x, n)for i = 1 to M dow i (n) ← w i (n) + µe(x,n) D||D(x,n)|| 2 i (x, n)end forFigure 6.5: The pseudo-code for the active decision fusion algorithmto weather conditions and changes in illumination which makes it necessary todeploy an adaptive wildfire detection system. It is not feasible to develop onestrong fusion model with fixed weights in this setting with drifting nature. Anideal on-line active learning mechanism should keep track of drifts in video andadapt itself accordingly. The projections in Eqs. 6.19, 6.30, 6.35, and 6.36 adjustthe importance of individual sub-algorithms by updating the weights accordingto the decisions of the oracle.6.4 Experimental ResultsThe proposed wildfire detection scheme with LMS based active learning methodis implemented on a PC with an Intel Core Duo CPU 1.86GHz processor andtested with forest surveillance recordings captured from cameras mounted on topof forest watch towers near Antalya and Mugla regions in Turkey. The installedsystem successfully detected three forest fires in the summer of 2008.The proposed active decision fusion strategy is compared with the universallinear predictor (ULP) scheme proposed by Oza [58], [59], and Singer andFeder [74] for online active learning. In the ULP scheme, decisions of individual
CHAPTER 6. WILDFIRE DETECTION 100Universal Predictor(x,n)for i = 1 to M dow i (0) = 1 , InitializationMend forŷ u (x, n) = ∑ i v i(n)D i (x, n)if ŷ u (x, n) ≥ 0 thenreturn 1elsereturn -1end iffor i = 1 to M dov i (n + 1) =exp(− 1 2c l(y(x,n),D i(x,n)))∑j exp(− 1 2c l(y(x,n),D j(x,n)))l(y(x, n), D i (x, n)) = [y(x, n) − D i (x, n)] 2end forFigure 6.6: The pseudo-code for the universal predictoralgorithms are linearly combined similar to Eq. 6.9 as follows:ŷ u (x, n) = ∑ iv i (n)D i (x, n) (6.37)where the weights, v i (n), are updated according to the ULP algorithm, whichassumes that the data (or confidence values D i (x, n), in our case) is governed bysome unknown probabilistic model P [74]. The objective of a universal predictor isto minimize the expected cumulative loss. An explicit description of the weights,v i (n), of the ULP algorithm is given as follows:v i (n + 1) =exp(− 1 2c l(y(x, n), D i(x, n)))∑j exp(− 1 2c l(y(x, n), D j(x, n)))(6.38)where c is a normalization constant and the loss function for the i-th decisionfunction is:l(y(x, n), D i (x, n)) = [y(x, n) − D i (x, n)] 2 (6.39)The constant c is taken as 4 as indicated in [74]. The universal predictor basedalgorithm is summarized in Fig. 6.6.We also implemented the Weighted Majority Algorithm (WMA) as the decisionfusion step [47]. The WMA is summarized in Fig. 6.7 [58]. In WMA,as opposed to our method, individual decision values from sub-algorithms are
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viiiçin de işaret işleme yöntem
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Chapter 1IntroductionDynamic textur
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CHAPTER 1. INTRODUCTION 3No fire de
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Chapter 2Flame Detection in Visible
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