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Deep Agent with Self-learning for Human Events Recognition (DASHER)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-19-P-6013
Agency Tracking Number: F18B-002-0086
Amount: $149,967.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF18B-T002
Solicitation Number: 18.B
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-11
Award End Date (Contract End Date): 2020-03-11
Small Business Information
70 Westview Street Suite 100
Lexington, MA 02421
United States
DUNS: 965530517
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Pranab Banerjee
 Sr. Research Scientist
 (617) 583-5730
 pranab.banerjee@bostonfusion.com
Business Contact
 Richard Salvage
Phone: (617) 583-5730
Email: rich.salvage@bostonfusion.com
Research Institution
 Oregon State University
 Zachary Gill Zachary Gill
 
70 Westview Street Suite 100
Lexington, MA 02421
United States

 (541) 573-1794
 Nonprofit College or University
Abstract

Automated analysis of human activities and events on the ground from aerial videos is key to extracting critical situational awareness and gathering actionable intelligence. It poses multiple challenges, including dynamic backgrounds that can be confused with foreground, multiple simultaneous activities in the field of view, occurrence of unknown activities, and large data volume and velocity. The state-of-the-art tools are unreliable, limited in scope, relies on extensive training data, and primarily designed for videos taken at the ground level or for close-up shots. Our proposed DASHER technology will address all of the above challenges in activity recognition for aerial videos via an automated system of smart agents that are capable not only of modeling and inferring activities at various spatial, temporal, and contextual scales but also discovering and learning new activities with minimal human intervention. DASHER's key components are self-learning smart agents for real-time detection of human activities and events at multiple spatiotemporal and contextual scales; a graph fusion engine for collective analysis and inferring high-level semantics; and a dynamic context model for robust activity and event classification. DASHER requires minimal human intervention and can operate under small amount training data.

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

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