Model-based vehicle tracking from image sequences with an application to road surveillance

Wai Ying Kan; James V. Krogmeier; Peter C. Doerschuk

doi:10.1117/1.600747

1 June 1996 Model-based vehicle tracking from image sequences with an application to road surveillance

Wai Ying Kan, James V. Krogmeier, Peter C. Doerschuk

Optical Engineering, Vol. 35, Issue 6, (June 1996). https://doi.org/10.1117/1.600747

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.

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Wai Ying Kan, James V. Krogmeier, and Peter C. Doerschuk "Model-based vehicle tracking from image sequences with an application to road surveillance," Optical Engineering 35(6), (1 June 1996). https://doi.org/10.1117/1.600747

Published: 1 June 1996

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