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Carnegie Mellon Robotics Institute

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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 technique 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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): NavLab

Text Reference

Frank Dellaert and Chuck Thorpe, "Robust Car Tracking using Kalman filtering and Bayesian templates," Conference on Intelligent Transportation Systems, 1997.

BibTeX Reference

@inproceedings{Dellaert_1997_895,
   author = "Frank Dellaert and Chuck Thorpe",
   title = "Robust Car Tracking using Kalman filtering and Bayesian templates",
   booktitle = "Conference on Intelligent Transportation Systems",
   year = "1997",
}

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