A framework for activity detection in wide-area motion imagery
- Los Alamos National Laboratory
Wide-area persistent imaging systems are becoming increasingly cost effective and now large areas of the earth can be imaged at relatively high frame rates (1-2 fps). The efficient exploitation of the large geo-spatial-temporal datasets produced by these systems poses significant technical challenges for image and video analysis and data mining. In recent years there has been significant progress made on stabilization, moving object detection and tracking and automated systems now generate hundreds to thousands of vehicle tracks from raw data, with little human intervention. However, the tracking performance at this scale, is unreliable and average track length is much smaller than the average vehicle route. This is a limiting factor for applications which depend heavily on track identity, i.e. tracking vehicles from their points of origin to their final destination. In this paper we propose and investigate a framework for wide-area motion imagery (W AMI) 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.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 962367
- Report Number(s):
- LA-UR-09-01629; LA-UR-09-1629; TRN: US200919%%127
- Resource Relation:
- Journal Volume: 7341; Conference: SPIE Defense, Security and Sensing ; April 13, 2009 ; Orlando, FL
- Country of Publication:
- United States
- Language:
- English
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