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

Award Information
Agency:
Department of Defense
Amount:
$150,000.00
Program:
SBIR
Contract:
N68335-12-C-0230
Solitcitation Year:
2012
Solicitation Number:
2012.1
Branch:
Navy
Award Year:
2012
Phase:
Phase I
Agency Tracking Number:
N121-017-0017
Solicitation Topic Code:
N121-017
Small Business Information
Vecna Technologies Inc.
6404 Ivy Lane, Suite 500, Greenbelt, MD, -
Hubzone Owned:
N
Woman Owned:
Y
Socially and Economically Disadvantaged:
N
Duns:
094078958
Principal Investigator
 Neal Checka
 Senior Research Scientist
 (617) 674-8545
 nchecka@vecna.com
Business Contact
 Michael Bearman
Title: VP/General Counsel
Phone: (240) 965-4500
Email: legal@vecna.com
Research Institution
 Stub
Abstract
Human operators must closely monitor video for simultaneous situational awareness and threat assessment. For instance, urban environments in a state of constant activity generate numerous visual cues, each of which must be examined so that potential security breaches do not go unnoticed. The need for constant vigilance places a significant burden on the human operator, invariably leading to fatigue and lapses in attention span. Vecna Robotics proposes a video event detection software tool, known as AESOP, that automatically detects time critical events in real-time. AESOP learns new events using a programming by example. With this technique, the analyst teaches the software tool new events by demonstrating actions on concrete examples. Once trained, AESOP processes incoming video and proactively identifies user-defined events in real-time while also indexing the video to simplify forensic analysis. Using state-of-the-art computer vision algorithms, the system identifies and tracks all targets in the scene. Characteristic features for each target are extracted over time yielding feature trajectories which are then efficiently matched to the trained event trajectories. An easy-to-use user interface allows the analyst to visualize and confirm detected events quickly. Furthermore, AESOP"s accuracy can improve by incorporating the confirmed detected events into the training set.

* information listed above is at the time of submission.

Agency Micro-sites

US Flag An Official Website of the United States Government