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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    Title :

    Similarity based vehicle trajectory clustering and anomaly detection


    Contributors:
    Zhouyu Fu, (author) / Weiming Hu, (author) / Tieniu Tan, (author)


    Publication date :

    2005-01-01


    Size :

    272432 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English