IARPA releases Video LINCS BAA

On May 21, the Intelligence Advanced Research Projects Activity (IARPA) released the broad agency announcement (IARPA-BAA-24-04) for the Video Linking and Intelligence from Non-Collaborative Sensors (Video LINCS) Program. Questions are due by 4:00 p.m. Eastern on June 5, and proposals are due by 4:00 p.m. on July 15.

IARPA is seeking innovative solutions for the Video LINCS Program in this BAA, which is envisioned to be a 48-month effort across all technical areas. The Video LINCS program aims to develop novel capabilities to autonomously re-identify objects across diverse video sensor collections and map all objects to a common reference frame.

In today’s world, video is a ubiquitous sensor. Voluminous video data is collected (e.g., security, closed circuit television (CCTV), aerial, etc.), but the manpower and available attention to provide desired security is limited. Operators are overloaded with many video feeds with limited attention for the available data volumes or ability to discern threats. Tragic incidents trigger forensic analyses that require significant manpower to pore through troves of imagery.

Diverse video sensors, even when installed at a single facility, often do not collaborate with each other. Some automated analytics have been developed, ranging from motion detection to object classification to summarization, providing valuable tools for filtering video content. These capabilities assist operators in focusing their attention. Ultimately, the burden on operators is high, requiring significant expertise, recall and resources to associate content across footage and discern threats.

The goal of the Video LINCS program, it to develop re-identification (reID) algorithms to autonomously associate objects across diverse, non-collaborative, video sensor footage and map re-identified objects to a unified coordinate system (geo-localization). The reID and geo-localization algorithms will distill raw pixel data into spatio-temporal motion vectors, providing the ability to analyze these patterns for anomalies and threats. While the ultimate goal will be to re-identify general objects, the program will start with person reID, progress to vehicle reID, and culminate with reID of generic objects across a video collection.

In the overall program design, a diverse collection of video imagery from different sensors is input into the Video LINCS system that reidentifies objects across the collection and geo-localizes those objects, providing tracks for each object in both the initial camera coordinate and common geo-coordinate reference frames. Offerors to the Video LINCS program shall propose solutions for both the re-identification and geo-localization objectives.

Review the IARPA Video LINCS BAA.

Source: SAM

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