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Algorithm-Based People Detection and Threat Determination from Passive Infrared and Visible Cameras

Award Information
Agency: Department of Defense
Branch: Army
Contract: W909MY-22-C-0011
Agency Tracking Number: A2-8848
Amount: $1,778,995.16
Phase: Phase II
Program: SBIR
Solicitation Topic Code: A19-052
Solicitation Number: 19.1
Timeline
Solicitation Year: 2019
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-05-27
Award End Date (Contract End Date): 2024-09-26
Small Business Information
1712 Route 9 Suite 300
Clifton Park, NY 12065-3104
United States
DUNS: 010926207
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Daniel Davila
 (770) 630-6497
 daniel.davila@kitware.com
Business Contact
 Ashley Carbino
Phone: (518) 836-2173
Email: ashley.carbino@kitware.com
Research Institution
N/A
Abstract

In the modern battlefield, an increasing prevalence of light-weight, low-cost electro-optical (EO) and infrared (IR) cameras worn by soldiers, mounted on ground vehicles, and carried by small unmanned aircraft systems (sUAS) extends battlespace monitoring to unprecedented levels. However, simultaneously monitoring all of these video streams leads to information overload for human operators. To autonomously attend to all of these feeds, we propose the Panoptic Guardian VTA (Video Threat Analysis) system. Panoptic Guardian deploys advanced artificial intelligence algorithms to autonomously detect and classify potential threats in real time, while distilling away innocuous distractions. The Panoptic Guardian algorithms are run at the edge, such as onboard a sUAS, and can detect people across a variety of size scales, supports day/night video operations (EO/IR), and can classify a multitude of activities of interest to be disseminated over the network. The system captures a rich set of activities and attributes, building up an ontology of threat indicators so that more-complex threats can be identified, such as operation of anti-tank guided missile (ATGM) systems, coordinated military maneuvers, or burying improvised explosive devices. Its novel decomposition of the threat-analysis pipeline allows more-economical training of deep neural networks without expensive, comprehensive data collections.

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

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