Fire Detection Algorithms Using Multimodal ... - Bilkent University

CHAPTER 6. WILDFIRE DETECTION 101Weighted Majority(x,n)for i = 1 to M dow i (0) = 1 , InitializationMend forif ∑ i:d i (x,n)=1 w i(n) ≥ ∑ i:d i (x,n)=−1 w i(n) thenreturn 1elsereturn -1end iffor i = 1 to M doif d i (x, n) ≠ y thenw i (n + 1) ← w i(n)2end ifend forFigure 6.7: The pseudo-code for the Weighted Majority Algorithmbinary, i.e., d i (x, n) ∈ {−1, 1}, which are simply the quantized version of real valuedD i (x, n) defined in Section 6.2. In the WMA, the weights of sub-algorithmsyielding contradictory decisions with that of the oracle are reduced by a factor oftwo in an un-controlled manner, unlike the proposed LMS based algorithm andthe ULP scheme. Initial weights for WMA are taken as 1 , as in the proposedMLMS based scheme.The LMS based scheme, the ULP based scheme, the WMA based scheme, andthe non-adaptive approach with fixed weights are compared with each other inthe following experiments. In Tables 6.1 and 6.2, 6-hour-long forest surveillancerecordings containing actual forest fires and test fires as well as video sequenceswith no fires are used.We have 7 actual forest fire videos and 5 test fire videos ranging from 2 kmto 8 km captured in Antalya and Mugla regions in Turkey, in the summers of2007 and 2008. All of the above mentioned decision fusion methods detect forestfires within 8 seconds, as shown in Table 6.1. The detection rates of the methodsare comparable to each other. On the other hand, the proposed adaptive fusionstrategy significantly reduces the false alarm rate of the system by integrating thefeedback from the guard (oracle) into the decision mechanism within the activelearning framework described in Section 6.3.In Fig. 6.8 a typical false alarm