Fire Detection Algorithms Using Multimodal ... - Bilkent University

CHAPTER 6. WILDFIRE DETECTION 104Table 6.1: Frame numbers at which an alarm is issued with different methods forwildfire smoke captured at various ranges and fps. It is assumed that the smokestarts at frame 0.Video Range Capture Frame number at which an alarm is issuedSequence (km) Frame Rate LMS Universal WMA Fixed(fps) Based Based WeightsV1 4 7 24 22 28 20V2 8 7 44 48 40 51V3 2 7 28 35 38 29V4 3 5 33 38 26 37V5 5 10 58 67 69 41V6 6 10 40 41 38 32V7 6 10 38 36 30 35V8 6 10 53 57 56 47V9 6 10 67 71 56 71V10 3 5 28 34 32 35V11 5 7 42 40 36 39V12 5 7 51 55 54 446.5 SummaryAn automatic wildfire detection algorithm using an LMS based active learning capabilityis developed. The compound algorithm comprises of four sub-algorithmsyielding their own decisions as confidence values in the range [−1, 1] ∈ R. TheLMS based adaptive decision fusion strategy takes into account the feedback fromguards of forest watch towers. Experimental results show that the learning durationis decreased with the proposed online active learning scheme. It is alsoobserved that false alarm rate of the proposed LMS based method is the lowest inour data set, compared to universal linear predictor (ULP) and weighted majorityalgorithm (WMA) based schemes.The tracking capability of the LMS algorithm is analyzed using a set theoreticframework. The proposed framework for decision fusion is suitable for problemswith concept drift. At each stage of the LMS algorithm, the method tracks thechanges in the nature of the problem by performing an orthogonal projectiononto a hyperplane describing the decision of the oracle.