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.
Rossi @cite_17 proposed a method to extract geometric fire characteristics using stereoscope videos. One of the steps is a segmentation based on a clustering algorithm, in which the image is divided into two clusters based on the channel V of the YUV color space. The cluster with the highest value of V corresponds to fire. Thereafter, Rossi used a 3D-Gaussian model to classify pixels as fire. In this method, the accuracy of the classification depends on a parameter provided by the user. This method presents limitations, since the authors assume that the fire is registered in a controlled environment.
@cite_17
1964084023
This paper presents a new instrumentation system, based on stereovision, for the visualization and quantitative characterization of fire fronts in outdoor conditions. The system consists of a visible pre-calibrated stereo camera and a computer with dedicated software. In the proposed approach, images are captured simultaneously and processed using specialized algorithms. These algorithms permit to model 3D fire fronts and extract geometric characteristics like volume, surface area, heading direction and length. Experiments were carried out in outdoor scenarios and the obtained results show the efficiency of the proposed system. This system successfully measures 3D geometric parameters of fire fronts over a range of combustible and environmental conditions.