Large Scale Organization and Inference of an Imagery Dataset for Public Safety
This work addresses the need for efficient data management in public safety video analytics, but it is incremental as it applies existing methods to a new dataset.
The researchers tackled the challenge of organizing and processing a large-scale public safety imagery dataset by developing a hierarchical organization approach and evaluating it through large-scale inference across terabytes of data, achieving efficient compute and storage.
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.