Mr. James R. Srinivasan Profile

James R. Srinivasan

at 2d3 USA

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Proceedings Article | 19 June 2014 Paper

Computer vision-based technologies and commercial best practices for the advancement of the motion imagery tradecraft

Marja Phipps, David Capel, James Srinivasan

Proceedings Volume 9089, 90890O (2014) https://doi.org/10.1117/12.2049051

KEYWORDS: Video, Cameras, Calibration, 3D modeling, Image fusion, Sensors, Unmanned aerial vehicles, Video processing, Motion analysis, Image processing

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Motion imagery capabilities within the Department of Defense/Intelligence Community (DoD/IC) have advanced significantly over the last decade, attempting to meet continuously growing data collection, video processing and analytical demands in operationally challenging environments. The motion imagery tradecraft has evolved accordingly, enabling teams of analysts to effectively exploit data and generate intelligence reports across multiple phases in structured Full Motion Video (FMV) Processing Exploitation and Dissemination (PED) cells. Yet now the operational requirements are drastically changing. The exponential growth in motion imagery data continues, but to this the community adds multi-INT data, interoperability with existing and emerging systems, expanded data access, nontraditional users, collaboration, automation, and support for ad hoc configurations beyond the current FMV PED cells. To break from the legacy system lifecycle, we look towards a technology application and commercial adoption model course which will meet these future Intelligence, Surveillance and Reconnaissance (ISR) challenges. In this paper, we explore the application of cutting edge computer vision technology to meet existing FMV PED shortfalls and address future capability gaps. For example, real-time georegistration services developed from computer-vision-based feature tracking, multiple-view geometry, and statistical methods allow the fusion of motion imagery with other georeferenced information sources - providing unparalleled situational awareness. We then describe how these motion imagery capabilities may be readily deployed in a dynamically integrated analytical environment; employing an extensible framework, leveraging scalable enterprise-wide infrastructure and following commercial best practices.

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