In this paper, we proposed a hierarchical clustering framework to classify vehicle motion trajectories in real traffic video based on their pairwise similarities. First raw trajectories are pre-processed and resampled at equal space intervals. Then spectral clustering is used to group trajectories with similar spatial patterns. Dominant paths and lanes can be distinguished as a result of two-layer hierarchical clustering. Detection of novel trajectories is also possible based on the clustering results. Experimental results demonstrate the superior performance of spectral clustering compared with conventional fuzzy K-means clustering and some results of anomaly detection are presented.


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    Titel :

    Similarity based vehicle trajectory clustering and anomaly detection


    Beteiligte:
    Zhouyu Fu, (Autor:in) / Weiming Hu, (Autor:in) / Tieniu Tan, (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    272432 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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