In this paper, we propose a novel image-based fire detection approach, which combines the power of modern deep learning networks with multidimensional texture analysis based on higher-order linear dynamical systems. The candidate fire regions are identified by a Faster R-CNN network trained for the task of fire detection using a set of annotated images containing actual fire as well as selected negatives. The candidate fire regions are projected to a Grassmannian space and each image is represented as a cloud of points on the manifold. Finally, a vector representation approach is applied aiming to aggregate the Grassmannian points based on a locality criterion on the manifold. For evaluating the performance of the proposed methodology, we performed experiments with annotated images of two different databases containing fire and fire-coloured objects. Experimental results demonstrate the potential of the proposed methodology compared to other state of the art approaches.
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL