N231-049 TITLE: DIGITAL ENGINEERING - Artificial Intelligence/Machine Learning Video Processing and Packaging

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Artificial Intelligence (AI)/Machine Learning (ML); Networked C3

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop an automated tool to identify video images of interest using Artificial Intelligence/Machine Learning (AI/ML) to be sent to warfighters in real time.

DESCRIPTION: Today�s military camera systems can collect vast amounts of video data. However, the copious amounts of data do not translate into actionable information because the video data is not analyzed until long after data collection has occurred. This problem is exacerbated for video collected from planes and helicopters, where weight and manning restrictions significantly reduce the amount of display space and human attention that can be devoted to video imagery in real time. This problem is particularly severe for maritime patrol and reconnaissance aircraft and maritime helicopters, where the proliferation of other sensor types reduces the emphasis on real-time video analysis. There are no known capabilities to solve this situation.

The Navy is seeking an automated tool to identify significant snippets of video data and package these snippets into minimized data packages using AI/ML so they can be transmitted to decision-makers in real time. This video curation and video compression can significantly improve situational awareness during tactical operations.

Advancement in deriving understanding from raw video has made great strides in areas such as facial recognition and the derivation of information from video obtained from self-driving vehicles. However, there has been less advancement in automated recognition of important anomalies in maritime video collected from aircraft.

The technology sought would initially flag potentially important video snippets to be triaged by a sensor operator aboard the aircraft but would mature over time to reduce the number of useless video snippets for each important video segment. As the concept of operations associated with the envisioned AI/ML curation of tactically significant video is still uncertain, the exact requirements to be levied on the technology sought by this topic cannot yet be determined. However, it should be presumed that the operator aboard the aircraft would not be able to devote more the 5 minutes per hour to validation of AI/ML-curated snippets.

The Tactical Common Data Link (TCDL) used by maritime aircraft is capable of greater bandwidth than prior tactical data link networks, such as Link 16. However, curating important snippets is not sufficient if the size of the resulting video cut is too large to pass in real time. Therefore, a complementary innovation in compression and packaging of the video is required to minimize the size of the data transmitted while retaining the original resolution.

The combination of high-qualify AI/ML curation of maritime video with minimal snippet size at required video quality will provide Theater Undersea Warfare (TUSW) decision makers accessing the Networked Architecture for Undersea Theater Integrated C2 Advantage (NAUTICA) system of systems and particularly the AN/UYQ-100 Undersea Warfare Decision Support System (USW-DSS) access to a wealth of crucial video information they often do not see until hours after the information has become of little importance.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. Owned and Operated with no Foreign Influence as defined by DOD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence Security Agency (DCSA), formerly the Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this contract as set forth by DSS and NAVSEA in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advance phases of this contract.

All DoD Information Systems (IS) and Platform Information Technology (PIT) systems will be categorized in accordance with Committee on National Security Systems Instruction (CNSSI) 1253, implemented using a corresponding set of security controls from National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53, and evaluated using assessment procedures from NIST SP 800-53A and DoD-specific (KS) (Information Assurance Technical Authority (IATA) Standards and Tools).

The Contractor shall support the Assessment and Authorization (A&A) of the system. The Contractor shall support the government�s efforts to obtain an Authorization to Operate (ATO) in accordance with DoDI 8500.01 Cybersecurity, DoDI 8510.01 Risk Management Framework (RMF) for DoD Information Technology (IT), NIST SP 800-53, NAVSEA 9400.2-M (October 2016), and business rules set by the NAVSEA Echelon II and the Functional Authorizing Official (FAO). The Contractor shall design the tool to their proposed RMF Security Controls necessary to obtain A&A. The Contractor shall provide technical support and design material for RMF assessment and authorization in accordance with NAVSEA Instruction 9400.2-M by delivering OQE and documentation to support assessment and authorization package development.

Contractor Information Systems Security Requirements. The Contractor shall implement the security requirements set forth in the clause entitled DFARS 252.204-7012, "Safeguarding Covered Defense Information and Cyber Incident Reporting," and National Institute of Standards and Technology (NIST) Special Publication 800-171.

PHASE I: Develop a concept for using AI/ML to curate maritime video and compress the curated video snippets for timely transmission (within minutes). Demonstrate that the concept meets key attributes as discussed in the Description Feasibility will be demonstrated through modeling or analysis. The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Develop and deliver a prototype video processing and packaging system that meets the parameters in the Description from the concept developed in Phase I. The prototype system will be trained using trothed video recordings from a range of significant and representative TUSW exercises and tested using trothed video recordings from TUSW exercises and operations other than the video used for training.

It is possible that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Support the Navy in transitioning the technology to Navy use. Work with Navy subject matter experts to determine the range of acceptable validation by sailors aboard the aircraft and required image quality as a function of visual conditions. In the event the Navy determines that the technologies are appropriate for incorporation into video systems for maritime aircraft, the Navy will work with the company and the prime for the maritime video systems to refine system requirements and either levy the improved requirement on prime contractors producing video systems or will purchase expeditionary prototypes and low rate initial production (LRIP) units from the company.

Potential dual use of the maritime video curation and packaging could be for search and rescue associated with losses over large bodies of water. The technology could also be deemed appropriate for curation of other sensor collections over vast areas (radar, lidar), depending on the capabilities of the company�s innovative technologies.

REFERENCES:

1.       Navy Fact File, "P-8A Poseidon Multi-mission Maritime Aircraft (MMA)," US Navy Office of Information, 23 Apr 2021. https://www.navy.mil/Resources/Fact-Files/Display-FactFiles/Article/2166300/p-8a-poseidon-multi-mission-maritime-aircraft-mma/

2.       Navy Fact File, "MH-60R Seahawk," US Navy Office of Information, 26 Oct 2021 . https://www.navy.mil/Resources/Fact-Files/Display-FactFiles/Article/2166679/mh-60r-seahawk/

3.       Navy Fact File, "AN/UYQ-100 Undersea Warfare Decision Support System (USW-DSS)." U.S. Navy Office of Information, 20 Sep 2021. https://www.navy.mil/Resources/Fact-Files/Display-FactFiles/Article/2166791/anuyq-100-undersea-warfare-decision-support-system-usw-dss

 

KEYWORDS: Theater Undersea Warfare; TUSW; video curation; artificial intelligence and machine learning; AI/ML; Networked Architecture for Undersea Theater Integrated C2 Advantage; NAUTICA; Undersea Warfare Decision Support System; USW-DSS; video compression in real

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