%0 Conference Proceedings
%T BoWFire: detection of fire in still images by integrating pixel color and texture analysis
%D 2015
%A Chino, Daniel Yashinobu Takada,
%A Avalhais, Letricia Pereira Soares,
%A Rodrigues Junior, Jose Fernando,
%A Traina, Agma Juci Machado,
%@affiliation University of Sao Paulo
%@affiliation University of Sao Paulo
%@affiliation University of Sao Paulo
%@affiliation University of Sao Paulo
%E Papa, Joćo Paulo,
%E Sander, Pedro Vieira,
%E Marroquim, Ricardo Guerra,
%E Farrell, Ryan,
%B Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)
%C Salvador
%8 Aug. 26-29, 2015
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K fire detection, still images, pixel-color classification, texture feature.
%X Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing 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 superpixel 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.
%@language en
%3 PID3758331_cameraReady.pdf