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Unsupervised Pattern Analysis of Vehicle Tracks

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: N/A
Agency Tracking Number: 36497
Amount: $98,989.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Solicitation Year: N/A
Award Year: 1997
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
Executive Place Iii, 50 Mall Road
Burlington, MA 01803
United States
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Simon Streltsov
 (617) 273-3388
Business Contact
Phone: () -
Research Institution

In this effort ALPHATECH will develop algorithms for extraction of purposeful temporal-spatial patterns from moving vehicle data and demonstrate their efficiency on a set of simulated MPA scenarios. We apply unsupervised learning algorithms to find similarities between traffic activities. Traffic activities are constructed using ALPHATECH's multiple hypothesis tracking algorithm and grouping correlated tracks into most probable activities. We use clustering techniques in order to identify motion patterns as groups of similar activities where similarity is defined based on the features inferred from MTI observations. In order to develop a full set of traffic activity features, we will perform Bayes Net estimation of kinematic and motion vehicle/group attributes and traffic centers analysis. Discovered clusters can be used to describe motion patterns in high-level terms for interaction with experts, to build statistical models of the motion patterns and ambient traffic, and to predict future traffic activities. We will perform feasibility and feature importance analysis by simulating known military patterns embedded into ambient traffic. Scenarios will be generated using existing ALPHATECH behavior pattern and motion target generators.

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

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