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Optical Engineering

Model-based vehicle tracking from image sequences with an application to road surveillance
Author(s): Wai Ying Kan; James V. Krogmeier; Peter C. Doerschuk
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Paper Abstract

A model-based approach to vehicle tracking is proposed and applied to a highway traffic surveillance problem, which is motivated by current research in intelligent transportation systems. Systems for traffic management and traveler information services require accurate and wide-area estimates of vehicle velocity and traffic spatial and temporal densities. A detection and tracking algorithm is developed that achieves good performance with complexity low enough for real-time implementation using inexpensive microprocessors. Detection thresholds are computed based on a statistical model for vehicle and background, and the theoretical detector performance is derived. The tracking algorithm filters position estimates from the detection algorithm using a simple vehicle dynamic model and the Kalman filter. Data association is accomplished with a nearest neighbor filter coupled with a lane-change handling logic.

Paper Details

Date Published: 1 June 1996
PDF: 7 pages
Opt. Eng. 35(6) doi: 10.1117/1.600747
Published in: Optical Engineering Volume 35, Issue 6
Show Author Affiliations
Wai Ying Kan, Purdue Univ. (United States)
James V. Krogmeier, Purdue Univ. (United States)
Peter C. Doerschuk, Purdue Univ. (United States)


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