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STAN: Scene and Target Analysis Network

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8651-18-P-0059
Agency Tracking Number: F18A-014-0018
Amount: $149,940.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF18A-T014
Solicitation Number: 2018.0
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-31
Award End Date (Contract End Date): 2019-07-31
Small Business Information
500 West Cummings Park
Woburn, MA 01801
United States
DUNS: 859244204
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Jasmin Leveille
 (781) 933-5355
Business Contact
 Lora Loyall
Phone: (781) 933-5355
Research Institution
 Boston University
 Andrew Freed
Trustees of Boston University 881 Commonwealth Avenue
Boston, MA 02215
United States

 (617) 358-7442
 Nonprofit College or University

The introduction of computer vision in precision-guided ammunition will potentially increase their targeting capabilities, in particular for moving targets such as tanks, vehicles and mobile command posts. EO/IR cameras offer a relatively inexpensive, low-SWaP payload solution which, combined with latest advances in SIMD hardware (GPUs), are finally starting to allow running sophisticated visual guidance algorithms onboard. Although modern computer vision may confer great benefits in targeting precision and accuracy, it also suffers from long-standing problems such as partial occlusion. We propose to tackle occlusion by leveraging advances in computational neuroscience into a recent deep neural network for target detection. In our algorithm, a part-based target model works jointly with an occlusion model. The part-based model decomposes target signatures in parts, and the occlusion model decomposes the scene in terms of occluded/occluders. By requiring the detection of an occlusion when only object part(s) are visible, the combined model will be more robust to false positives, avoiding the issue that individual parts alone are hard to distinguish from background clutter. The focus of the Phase I efforts is (1) the development of a vision-based algorithm for object detection with occlusion, and (2) concept demonstrations and performance characterization on representative imagery.

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

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