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19 May 2011 Multisensor ISR in geo-registered contextual visual dataspace (CVD)
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Abstract
Current ISR (Intelligence, Surveillance, and Reconnaissance) systems require an analyst to observe each video stream, which will result in analyst overload as systems such as ARGUS or Gorgon Stare come into use with many video streams generated by those sensor platforms. Full exploitation of these new sensors is not possible using today's one video stream per analyst paradigm. The Contextual Visual Dataspace (CVD) is a compact representation of real-time updating of dynamic objects from multiple video streams in a global (geo-registered/annotated) view that combines automated 3D modeling and semantic labeling of a scene. CVD provides a single integrated view of multiple automatically-selected video windows with 3D context. For a proof of concept, a CVD demonstration system performing detection, localization, and tracking of dynamic objects (e.g., vehicles and pedestrians) in multiple infrastructure camera views was developed using a combination of known computer vision methods, including foreground detection by background subtraction, ground-plane homography mapping, and appearance model-based tracking. Automated labeling of fixed and moving objects enables intelligent context-aware tracking and behavior analysis and will greatly improve ISR capabilities.
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Kyungnam Kim, Yuri Owechko, Arturo Flores, and Dmitriy Korchev "Multisensor ISR in geo-registered contextual visual dataspace (CVD)", Proc. SPIE 8049, Automatic Target Recognition XXI, 80490W (19 May 2011); https://doi.org/10.1117/12.887618
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