Papers › Large Scale Organization and Inference of an Imagery Dataset for Public Safety

Large Scale Organization and Inference of an Imagery Dataset for Public Safety

16 Aug 2019arXiv:1908.09006archive 2025-07-28

Jeffrey Liu, David Strohschein, Siddharth Samsi, Andrew Weinert

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.

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