This paper presents a mobile mapping system that aims at automatic detection of traffic infrastructure from image sequences. The visual information will be georeferenced with GPS/INS information to represent a most realistic model of the environment in a GIS database. The integration of innovative methods such as vertical structure segmentation and probabilistic object matching provides a qualified framework for the detection of arbitrary traffic infrastructure. The detection of infrastructure starts with a segmentation of the video frame into regions of interest by exploiting track localization and a-priori knowledge about the visual scene. A further step concerns the extraction of 3D structure and the segmentation of its associated distance map w.r.t. vertically accentuated objects. Color information derived from learned classification filters contributes to a characterization of class-specific support regions for further processing. Eventually, traffic signs and lights are robustly identified by probabilistic matching using a RBF neural network that was trained from real imagery of traffic infrastructure.


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    Title :

    Mobile detection of traffic infrastructure


    Contributors:
    Paletta, L. (author) / Paar, G. (author) / Wimmer, A. (author)


    Publication date :

    2001-01-01


    Size :

    645436 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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