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Full-motion video (FMV) target tracking requires the objects of interest be detected in a continuous video stream. Maintaining a stable track can be challenging as target attributes change over time, frame-rates can vary, and image alignment errors may drift. As such, optimizing FMV target tracking performance to address dynamic scenarios is critical. Many target tracking algorithms do not take advantage of parallelism due to dependencies on previous estimates which results in idle computation resources when waiting for such dependencies to resolve. To address this problem, a container-based virtualization technology is adopted to make more efficient use of computing resources for achieving an elastic information fusion cloud. In this paper, we leverage the benefits provided by container-based virtualization to optimize an FMV target tracking application. Using OpenVZ as the virtualization platform, we parallelize video processing by distributing incoming frames across multiple containers. A concurrent container partitions video stream into frames and then resembles processed frames into video output. We implement a system that dynamically allocates VE computing resources to match frame production and consumption between VEs. The experimental results verify the viability of containerbased virtualization for improving FMV target tracking performance and demostrates a solution for mission-critical information fusion tasks.

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