Title :
Clustering of Vehicle Trajectories
Author :
Atev, Stefan ; Miller, Grant ; Papanikolopoulos, Nikolaos P.
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Minnesota, Minneapolis, MN, USA
Abstract :
We present a method that is suitable for clustering of vehicle trajectories obtained by an automated vision system. We combine ideas from two spectral clustering methods and propose a trajectory-similarity measure based on the Hausdorff distance, with modifications to improve its robustness and account for the fact that trajectories are ordered collections of points. We compare the proposed method with two well-known trajectory-clustering methods on a few real-world data sets.
Keywords :
automated highways; computer vision; pattern clustering; spectral analysis; unsupervised learning; Hausdorff distance; automated vision system; spectral clustering; trajectory-clustering method; trajectory-similarity measure; unsupervised learning; vehicle trajectory; Clustering methods; Computer science; Euclidean distance; Layout; Machine vision; Principal component analysis; Robustness; Transportation; Unsupervised learning; Vehicles; Clustering of trajectories; time-series similarity measures; unsupervised learning;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2010.2048101
