Multimodal vision in and beyond the visible spectrum for security applications: wildland fires scenarios

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Recent years have witnessed an increased interest in infrared imaging and multimodal vision; both from the research community and industry. This interest has been mainly driven by the decrease in camera prices and an increase in their performance. Modalities that were once mostly limited to the military, are now expanding into commercial and industrial applications. This expansion is accompanied by a demand for new advanced signal and image processing algorithms to solve problems in a variety of new emerging areas of application using multimodal vision in and beyond the visible spectrum. In recent years, many algorithms have been developed for infrared and multimodal vision in the areas of face recognition, forest fires study, target tracking, non-destructive testing, etc. This work aims to develop new algorithms in multimodal vision applied to security applications, in particular the developement of multimodal vision systems in unstructured and natural disasters scenarios such as wildland and forest fires. The work will be conducted in four complementary axes aiming to build a more complete fire study and firefighting multimodal system: 1. Detection and segmentation of fires in outdoor unstructured scenarios using machine learning and multimodal vision systems in ground based and aerial vehicles. 2. Extraction of three-dimensional fire characteristics using multimodal cooperative vision systems (ground and aerial). 3. Registration of ground and aerial 3D data. 4. Prediction and detection of fire reignition. This work will contribute to advancing our knowledge in the areas of wildland fires research, multi-UAV (Unmanned Aerial Vehicles) vision systems, and in deep learning applied to unstructured scenario images. The developed tools will be further adapted to other natural risk scenarios and search & rescue applications. The scientific and technological outcomes will have important socioeconomic and environmental impacts. Through these developments, Canada can play a leading role in the area of technologies applied to natural risk scenarios. Finally, this work will enhance the training of highly qualified personnel.