Robust car tracking using Kalman filtering and Bayesian templates

Frank Dellaert; Chuck E. Thorpe

doi:10.1117/12.300869

27 January 1998 Robust car tracking using Kalman filtering and Bayesian templates

Frank Dellaert, Chuck E. Thorpe

Proceedings Volume 3207, Intelligent Transportation Systems; (1998) https://doi.org/10.1117/12.300869
Event: Intelligent Systems and Advanced Manufacturing, 1997, Pittsburgh, PA, United States

Abstract

We present a real-time model-based vision approach for detecting and tracking vehicles from a moving platform. It was developed in the context of the CMU Navlab project and is intended to provide the Navlabs with situational awareness in mixed traffic. Tracking is done by combining a simple image processing techniques with a 3D extended Kalman filter and a measurement equation that projects from the 3D model to image space. No ground plane assumption is made. The resulting system runs at frame rate or higher, and produces excellent estimates of road curvature, distance to and relative speed of a tracked vehicle. We have complemented the tracker with a novel machine learning based algorithm for car detection, the CANSS algorithm, which serves to initialize tracking.

Citation Download Citation

Frank Dellaert and Chuck E. Thorpe "Robust car tracking using Kalman filtering and Bayesian templates", Proc. SPIE 3207, Intelligent Transportation Systems, (27 January 1998); https://doi.org/10.1117/12.300869

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