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.
@article{arxiv.1908.09006,
title = {Large Scale Organization and Inference of an Imagery Dataset for Public Safety},
author = {Jeffrey Liu and David Strohschein and Siddharth Samsi and Andrew Weinert},
journal= {arXiv preprint arXiv:1908.09006},
year = {2019}
}