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28 April 2009 Automated UAV-based mapping for airborne reconnaissance and video exploitation
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Abstract
Airborne surveillance and reconnaissance are essential for successful military missions. Such capabilities are critical for force protection, situational awareness, mission planning, damage assessment and others. UAVs gather huge amount of video data but it is extremely labour-intensive for operators to analyse hours and hours of received data. At MDA, we have developed a suite of tools towards automated video exploitation including calibration, visualization, change detection and 3D reconstruction. The on-going work is to improve the robustness of these tools and automate the process as much as possible. Our calibration tool extracts and matches tie-points in the video frames incrementally to recover the camera calibration and poses, which are then refined by bundle adjustment. Our visualization tool stabilizes the video, expands its field-of-view and creates a geo-referenced mosaic from the video frames. It is important to identify anomalies in a scene, which may include detecting any improvised explosive devices (IED). However, it is tedious and difficult to compare video clips to look for differences manually. Our change detection tool allows the user to load two video clips taken from two passes at different times and flags any changes between them. 3D models are useful for situational awareness, as it is easier to understand the scene by visualizing it in 3D. Our 3D reconstruction tool creates calibrated photo-realistic 3D models from video clips taken from different viewpoints, using both semi-automated and automated approaches. The resulting 3D models also allow distance measurements and line-of- sight analysis.
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Stephen Se, Pezhman Firoozfam, Norman Goldstein, Linda Wu, Melanie Dutkiewicz, Paul Pace, and J. L. Pierre Naud "Automated UAV-based mapping for airborne reconnaissance and video exploitation", Proc. SPIE 7307, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VI, 73070M (28 April 2009); https://doi.org/10.1117/12.820225
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