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Foveated Video Object Recognition

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0199
Agency Tracking Number: N14A-008-0092a
Amount: $3,571,807.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N14A-T008
Solicitation Number: 14.A
Timeline
Solicitation Year: 2014
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-03-19
Award End Date (Contract End Date): 2023-03-17
Small Business Information
5266 Hollister Avenue, Suite 229
Santa Barbara, CA 93111-0000
United States
DUNS: 097607852
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Elliot Staudt
 Research Staff Member
 (805) 967-9828
 staudt@mayachitra.com
Business Contact
 Bangalore S. Manjunath
Phone: (805) 448-8227
Email: manj@mayachitra.com
Research Institution
N/A
Abstract

Low resolution quality of operational data, size of objects of interest, view occlusions, and crowded scenes degrade the performance of state-of-art region saliency and object recognition approaches when applied to overhead sensor data. We have developed a technology to automatically detect and recognize multitude of objects of potential interest providing an object recognition decision with a high level of confidence, and which can effectively and efficiently perform in real to near-real time on medium to high end desk top computer. Our technology employs state-of-art deep learning and bio-inspired methods to efficiently and effectively detect small objects in overhead noisy (crowded occluded) videos, where state-of-art models fail. The technology comes with the suite of the support modules that enhance user interaction and modeling. We propose to advance the Foveated Video Object Detection and Recognition technology into a suite of libraries and executables to meet the form factor and be integrated into PMA-281 projects, Common Control System (CCS) and Digital Camera Receiving Station (DCRS).

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

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