A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots

This paper proposes an automatic fire detection system that is able to work with both stationary and non-stationary video achieving a 91.90% accuracy using color, texture and shape information. As an extension to prior approaches in the video-based fire detection field, the proposed system replaces the use of threshold-driven color spectroscopy with a trained classifier, resulting in a better color segmentation. From the color-classified regions, non-temporal texture features are extracted and used as an input to a trained Random Forests classifier. Using standardized metrics and a large dataset we compare our solution to other state of the art systems, showing that it outperforms the prior approaches in both frame by frame classification and fire region location. The method has been validated over more than 24 thousand frames proving to offer high precision and recall.