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Object Counting using Unified LAtent Representation (OCULAR)

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047620C0056
Agency Tracking Number: NGA-P2-21-04
Amount: $999,991.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: NGA191-009
Solicitation Number: 19.1
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2020-11-12
Award End Date (Contract End Date): 2022-11-29
Small Business Information
601 Hutton St STE 109
Raleigh, NC 27606-6322
United States
DUNS: 148551653
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nikhil Kriplani
 (919) 341-8241
Business Contact
 Gary Edge
Phone: (919) 949-4111
Research Institution

Vadum will develop and implement a novel, unsupervised deep-learning approach to estimate the number of objects in a SAR image automatically, accurately and with low-latency. The approach learns a unique representation of a SAR image that is resilient to a wide range of SAR artifacts, such as geometric and temporal image misalignments, resolutions, noise and collection geometries. The technique architecture is extensible to fusing images collected from other radar imagery sensors, as well Infra-red (IR) and Hyper-Spectral Imaging (HSI) sensors enabling high quality, all-weather data collection capabilities. It can be deployed in a number of scenarios: An analyst can depend on automatic, accurate counting estimates and focus on complex operations such as scene situational understanding; At scale deployment in distributed environments simultaneously covering large swaths of geographical areas; Standalone deployment in edge environments such as on-demand scene understanding for remote piloted aircraft.

* Information listed above is at the time of submission. *

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