This paper proposes a semantic mapping method based on pedestrian activity in the urban road environment. Pedestrian activity patterns are learned from pedestrian tracks collected by a mobile platform. With the learned knowledge of pedestrian activity, semantic mapping is performed using Bayesian classification techniques. The proposed method is tested in real experiments, and shows promising results in recognizing four activity-related semantic properties of the urban road environment: pedestrian path, entrance/exit, pedestrian crossing and sidewalk.


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    Title :

    Learning pedestrian activities for semantic mapping


    Contributors:
    Qin, B. (author) / Chong, Z. J. (author) / Bandyopadhyay, T. (author) / Ang, M. H. (author) / Frazzoli, E. (author) / Rus, D. (author)


    Publication date :

    2014-05-01


    Size :

    2089910 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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