This study introduces an approach to three-dimensional vehicle pose estimation using a stereo camera system. After computation of stereo and optical flow on the investigated scene, a four-dimensional clustering approach separates the static from the moving objects in the scene. The iterative closest point algorithm (ICP) estimates the vehicle pose using a cuboid as a weak vehicle model. In contrast to classical ICP optimisation a polar distance metric is used which especially takes into account the error distribution of the stereo measurement process. The tracking approach is based on tracking-by-detection such that no temporal filtering is used. The method is evaluated on seven different real-world sequences, where different stereo algorithms, baseline distances, distance metrics, and optimisation algorithms are examined. The results show that the proposed polar distance metric yields a higher accuracy for yaw angle estimation of vehicles than the common Euclidean distance metric, especially when using pixel-accurate stereo points.


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    Title :

    3D pose estimation of vehicles using a stereo camera


    Contributors:


    Publication date :

    2009-06-01


    Size :

    4322218 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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