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.


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    Title :

    Robust car tracking using Kalman filtering and Bayesian templates


    Contributors:

    Conference:

    Intelligent Transportation Systems ; 1997 ; Pittsburgh,PA,USA


    Published in:

    Proc. SPIE ; 3207 ; 72


    Publication date :

    1998-01-27





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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    Robust car tracking using Kalman filtering and Bayesian templates [3207-38]

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