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Proceedings Paper

Robust car tracking using Kalman filtering and Bayesian templates
Author(s): Frank Dellaert; Chuck E. Thorpe
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Paper 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.

Paper Details

Date Published: 27 January 1998
PDF: 12 pages
Proc. SPIE 3207, Intelligent Transportation Systems, (27 January 1998); doi: 10.1117/12.300869
Show Author Affiliations
Frank Dellaert, Carnegie Mellon Univ. (United States)
Chuck E. Thorpe, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 3207:
Intelligent Transportation Systems
Marten J. de Vries; Pushkin Kachroo; Kaan Ozbay; Alan C. Chachich, Editor(s)

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