2015 28th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (2015)
Salvador, Bahia, Brazil
Aug. 26, 2015 to Aug. 29, 2015
Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowd sourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on super pixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.
Image color analysis, Feature extraction, Videos, Training, Clustering algorithms, Color, Proposals,texture feature, fire detection, still images, pixel-color classification
Daniel Y. T. Chino, Letricia P. S. Avalhais, Jose F. Rodrigues, Agma J. M. Traina, "BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis", 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), vol. 00, no. , pp. 95-102, 2015, doi:10.1109/SIBGRAPI.2015.19