A Rubust System for Automated Video-Based Vehicle Recognition

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-04-C-R270
Agency Tracking Number: 03SB1-0213
Amount: $736,363.00
Phase: Phase II
Program: SBIR
Awards Year: 2004
Solicitation Year: 2003
Solicitation Topic Code: SB031-015
Solicitation Number: 2003.1
Small Business Information
6 New England Executive Park, Burlington, MA, 01803
DUNS: 094841665
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Matthew Antone
 Lead Research Engineer
 (781) 273-3388
Business Contact
 John Barry
Title: Contracts Manager
Phone: (781) 273-3388
Email: jbarry@alphatech.com
Research Institution
We propose an end-to-end video-based system for online class recognition of moving vehicles observed by a passive stationary camera. SAVOR (or System for Automated Video-based Object Recognition) continuously detects, tracks, and classifies vehicles within the camera's field of view in real time, additionally providing a live operator display and archiving data and results for later analysis. To our knowledge, SAVOR will represent the first continuously-operating passive vehicle recognition system in existence. The system augments a proven image-based Predict-Extract-Match-Search (PEMS) framework, employing pre-defined shape and appearance templates and exploiting the spatio-temporal coherence inherent in motion imagery to extract and correlate rich two- and three-dimensional feature sets. Calibration of geometric and photometric environmental attributes allows robust operation and consistent reasoning over a constrained metric parameter space, while a sophisticated appearance prediction engine able to account for such phenomena as reflectance and cast shadows additionally enables highly accurate template-to-image matching. SAVOR is designed to approach a vehicle type classification rate of 95% or greater on ordinary daytime traffic. Real-time operation allows statistically meaningful performance analyses over long durations and in temporally-varying environmental conditions. We will develop simple graphical interfaces for data analysis and truthing, and assess the system using isolated offline test cases for repeatable algorithmic evaluation as well as the live data stream for long-term analysis.

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

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