Automated Swimmer Recongition Algorithms for the HarborGuard Intrusion Detection System

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00030-07-C-0045
Agency Tracking Number: N043-228-0546
Amount: $599,942.00
Phase: Phase II
Program: SBIR
Awards Year: 2007
Solicitation Year: 2004
Solicitation Topic Code: N04-228
Solicitation Number: 2004.3
Small Business Information
1410 Sachem Place, Suite 202, Charlottesville, VA, 22901
DUNS: 120839477
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Jason Burkholder
 Sr. Research Scinetist
 (434) 973-1215
 burkholder@bainet.com
Business Contact
 David Ward
Title: President
Phone: (434) 973-1215
Email: barron@bainet.com
Research Institution
N/A
Abstract
The remote identification of surfaced swimmers in a harbor environment is a critical requirement for a comprehensive waterside security system. This requirement can be clearly divided into detection and classification objectives. Detection in this context means identifying the presence of a target on the surface of the water. Classification means discriminating between different types of targets - specifically, discriminating between swimmers on the surface and non-swimmer targets. In Phase I, Barron Associates, Inc. collaborated with L-3 Communications Klein Associates to develop highly-effective baseline automatic swimmer recognition (ASR) algorithms. The baseline ASR algorithms were demonstrated using a diverse set of radar data that included images of swimmers and other small targets, such as birds, buoys, and floats. Barron Associates extracted features from the radar images and classification algorithms to identify swimmers. The ASR algorithm achieved perfect classification of the radar targets in closed-set tests and, importantly, achieved excellent classification performance in open-set tests. The objectives of this Phase II SBIR effort are to: (1) implement the algorithms and supporting software for online, near real-time operation; (2) evaluate the algorithms in adverse environmental conditions; and (3)enhance the algorithms to incorporate additional sensor data, such as multiple radars, video/thermal images, and sonar signals.

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

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