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Leveraging Synthetic Data to train mission critical computer vision algorithms

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-20-P-0950
Agency Tracking Number: FX201-CSO1-0824
Amount: $50,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: J201-CSO1
Solicitation Number: X20.1
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-03-09
Award End Date (Contract End Date): 2020-06-09
Small Business Information
75 Broad Street, Suite 640
New York, NY 10004-1111
United States
DUNS: 080706249
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Seth Potter
 (917) 697-7005
 seth@aireverie.com
Business Contact
 Paul Walborsky
Phone: (617) 921-8800
Email: paul@aireverie.com
Research Institution
N/A
Abstract

AI.Reverie’s simulation platform leverages the power of synthetic data to significantly improve the performance of mission critical vision algorithms. By generating fully annotated synthetic data at scale, the platform offsets the costs and time to label real world images and videos.  The program objectives are focused on improving the accuracy of computer vision algorithms for object detection/tracking and activity recognition using fully annotated synthetic data for any type of sensor and at different altitudes (ground level to 64 square kilometers). AI.Reverie is solving the access to data, lack of data in conflict zones  and high annotation cost problems for the Army by generating unlimited amounts of fully annotated data at scale.   This  will help the Army to significantly improve the accuracy of object detection/tracking and activity recognition algorithms at a much lower cost than relying solely on real data and human-in-the-loop annotation solutions.  AI.Reverie will recognize profit by charging commercial customers initially in the CCTV, retail and supply chain markets who are all experiencing the same data issues which are driven by the lack of access to real data, high cost and error rates of human-in-the loop solutions as well as privacy concerns.

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

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