Automatic lane detection is known to facilitate the real-time traffic planning and identify traffic congestion. In this paper, we develop a visual surveillance trajectory clustering (VSTC) framework for automatic lane detection. Given a surveillance video, trajectories of vehicles are extracted at first. These trajectories contain behavior of vehicles on different lanes and are clustered by VSTC to retrieve candidate lanes. Finally, a density verification is applied to identify the correct lanes from candidate lanes. As shown in the experiments, our framework can identify the lanes by using trajectories without prior knowledge.


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    Titel :

    Lane detection in surveillance videos using vector-based hierarchy clustering and density verification


    Beteiligte:
    Teng, Shan-Yun (Autor:in) / Chuang, Kun-Ta (Autor:in) / Huang, Chun-Rong (Autor:in) / Li, Cheng-Chun (Autor:in)


    Erscheinungsdatum :

    2015-05-01


    Format / Umfang :

    843373 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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