Adaptive, Automated Real-time Event/Target Detection using Supervised Learning

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$79,994.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
N68335-12-C-0187
Award Id:
n/a
Agency Tracking Number:
N121-017-0198
Solicitation Year:
2012
Solicitation Topic Code:
N121-017
Solicitation Number:
2012.1
Small Business Information
28 Corporate Drive, Clifton Park, NY, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
010926207
Principal Investigator:
Harry Sun
Senior Research Engineer
(518) 836-2192
harry.sun@kitware.com
Business Contact:
Vicki Rafferty
Contracts Administrator
(518) 881-4401
vicki.rafferty@kitware.com
Research Institute:
Stub




Abstract
Automatic event and activity recognition from stream video and video archive can greatly enhance the effectiveness and efficiency of motion imagery analyst by filtering out the most relevant and timely content from a huge amount of video data through query video clips. Building on our DARPA VIRAT program to detect various events, such as vehicle starting, stopping, turning, U-turning, person running and so on, we propose to improve novel event detection by leveraging existing event modules to build the new model through semi-supervised learning and label expansion. The proposed approach will allow the user to annotate just a few training samples, and it will automatically label additional data based on other, existing event models, thus improving the new model over one built just on the user provided samples. We also propose to explore a novel multi-way interaction descriptor based on disjoint information that will be able to describe multi-entity events, and will improve the performance of single entity events by jointly using multiple features.

* information listed above is at the time of submission.

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