Fire Detection Algorithms Using Multimodal ... - Bilkent University

List of Tables1.1 Organization of this thesis. . . . . . . . . . . . . . . . . . . . . . . 72.1 The mean red, green and blue channel values and variances of tenGaussian distributions modeling flame color in Fig. 2.2 (b) are listed. 172.2 Comparison of the proposed method (Method-2.1), the methodbased on color and temporal variation clues only (Method-2.2)described in [64], and the method proposed in [17] (Method-2.3). . 332.3 Time performance comparison of Methods 2.1, 2.2, and 2.3 for themovies in Table 2.2. The values are the processing times per framein milliseconds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.1 Detection results for some of the test clips. In the video clip V3,flames are hindered by a wall for most of the time. . . . . . . . . . 513.2 Fire detection results of our method when trained with differentflame types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.3 Comparison of the proposed method with the modified version ofthe method in [36] (CVH method) and the fire detection methoddescribed in [87] for fire detection using a regular visible rangecamera. The values for processing times per frame are in milliseconds. 52xviii