The transition from static to motion imagery enabled by UAVs, and distributed sensor networks introduces significant challenges monitoring multiple video streams, indexing large amounts of video, and retrieving video segments with significant spatiotemporal events. Current analyst workflow systems support spatiotemporal analysis minimally: analysts roam through large image mosaics, revisit the same area, and make use of accompanying geographic information systems (GIS) databases to examine the evolution of a scene. However, the analyst still needs to decide whether the trajectory of a vehicle appears suspicious, whether a moving convoy resembles a formation of military vehicles, or whether the trajectory of a truck today resembles yesterday s pattern of movement of another vehicle. As data influx is increasing at substantially high rates, agencies are trying hard to keep up with tremendous amounts of incoming data, further exasperated during crises (e.g. military operations), where the rate of incoming information explode (e.g. as areas of interest are under heavy surveillance by fleets of UAVs).
Spatiotemporal Helix Approach to Geospatial Exploitation of Motion Imagery
2004
3 pages
Report
Keine Angabe
Englisch
Cartography , Natural Resources & Earth Sciences , Computer Software , Military operations , Data bases , Spatial distribution , Symposia , Information systems , Military vehicles , Unmanned , Mosaics(Detectors) , Video signals , Trajectories , Motion detectors , Component reports , Gis(Geographic information systems)
Motion imagery processing and exploitation (MIPE)
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