As wide-area persistent imaging systems become cost effective, increasingly large areas of the earth can be imaged at relatively high frame rates. Efficient exploitation of the large geo-spatial-temporal datasets produced by these systems poses significant technical challenges for image and video analysis and for data mining. Significant progress in image stabilization, moving object detection and tracking, are allowing automated systems to generate hundreds to thousands of vehicle tracks from raw data, with little human intervention. However, tracking performance at this scale is unreliable, and average track length is much smaller than the average vehicle route. These are limiting factors for applications that depend heavily on track identity, i.e. tracking vehicles from their points of origin to their final destination. In this paper, we propose and evaluate a framework for wide-area motion imagery (WAMI) exploitation that minimizes the dependence on track identity. In its current form, this framework takes noisy, incomplete moving object detection tracks as input, and produces a small set of activities (e.g. multi-vehicle meetings) as output. The framework can be used to focus and direct human users and additional computation, and suggests a path towards high-level content extraction by learning from the human-in-the-loop.
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