Title
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.
Year
DOI
Venue
2017
10.1109/IPTA.2017.8310085
2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)
Keywords
Field
DocType
Multimodal vision,infrared imaging,wildland fires,image processing,3D vision
Reference frame,Computer vision,Pattern recognition,Computer science,Decision support system,Projection (linear algebra),Image segmentation,Feature extraction,Inertial measurement unit,GNSS applications,Artificial intelligence,Firefighting
Conference
ISSN
ISBN
Citations 
2154-512X
978-1-5386-1843-1
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Moulay A. Akhloufi100.68
Tom Toulouse2151.56
Lucile Rossi3296.05
Xavier Maldague4122.66