  Video applications and analytics are routinely projected as a stressing and
significant service of the Nationwide Public Safety Broadband Network. As part
of a NIST PSCR funded effort, the New Jersey Office of Homeland Security and
Preparedness and MIT Lincoln Laboratory have been developing a computer vision
dataset of operational and representative public safety scenarios. The scale
and scope of this dataset necessitates a hierarchical organization approach for
efficient compute and storage. We overview architectural considerations using
the Lincoln Laboratory Supercomputing Cluster as a test architecture. We then
describe how we intelligently organized the dataset across LLSC and evaluated
it with large scale imagery inference across terabytes of data.
