Multimodal three-dimensional vision for wildland fires detection and analysis

Abstract

This paper proposes a new multimodal stereovision framework for wildland fires detection and analysis. The proposed system uses near infrared and visible images to robustly segment the fires and extract their three-dimensional characteristics during propagation. It uses multiple multimodal stereovision systems to capture complementary views of the fire front. A new registration approach is proposed, it uses multisensory fusion based on GNSS and IMU data to extract the projection matrix that permits the representation of the 3D reconstructed fire in a common reference frame. The fire parameters are extracted in 3D space during fire propagation using the complete reconstructed fire. The obtained results show the efficiency of the proposed system for wildland fires research and firefighting decision support in operational scenarios.