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Proceedings Paper

A framework for activity detection in wide-area motion imagery
Author(s): Reid Porter; Christy Ruggiero; John D. Morrison
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Paper Abstract

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.

Paper Details

Date Published: 27 April 2009
PDF: 10 pages
Proc. SPIE 7341, Visual Information Processing XVIII, 73410O (27 April 2009); doi: 10.1117/12.818629
Show Author Affiliations
Reid Porter, Los Alamos National Lab. (United States)
Christy Ruggiero, Los Alamos National Lab. (United States)
John D. Morrison, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 7341:
Visual Information Processing XVIII
Zia-Ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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